query: I am doing theoretical experiments on whether deep learning models can understand quantitative logic reasoning. To continue my experiment, I am looking for a deep learning model that can perform deductive and quantitative inference tasks. Deductive tasks include several premises and a conclusion, and the model needs to identify whether the conclusion is reasonable based on the premise; and quantitative inference problem needs the model to interpret real-life problems into math problems and then solve it. I hope the single model can handle both deductive, quantitative inference tasks and a task that is a combination of the 2 subtasks. Additionally, I need another system that is able to efficiently take answers from humans, extract the key part and compare human answers to the answers generated by the model.

positive:
Deep learning is very effective at jointly learning feature representations
and classification models, especially when dealing with high dimensional input
patterns. Probabilistic logic reasoning, on the other hand, is capable to take
consistent and robust decisions in complex environments. The integration of
deep learning and logic reasoning is still an open-research problem and it is
considered to be the key for the development of real intelligent agents. This
paper presents Deep Logic Models, which are deep graphical models integrating
deep learning and logic reasoning both for learning and inference. Deep Logic
Models create an end-to-end differentiable architecture, where deep learners
are embedded into a network implementing a continuous relaxation of the logic
knowledge. The learning process allows to jointly learn the weights of the deep
learners and the meta-parameters controlling the high-level reasoning. The
experimental results show that the proposed methodology overtakes the
limitations of the other approaches that have been proposed to bridge deep
learning and reasoning.

positive:
Recent years have witnessed the great success of deep neural networks in many
research areas. The fundamental idea behind the design of most neural networks
is to learn similarity patterns from data for prediction and inference, which
lacks the ability of logical reasoning. However, the concrete ability of
logical reasoning is critical to many theoretical and practical problems. In
this paper, we propose Neural Logic Network (NLN), which is a dynamic neural
architecture that builds the computational graph according to input logical
expressions. It learns basic logical operations as neural modules, and conducts
propositional logical reasoning through the network for inference. Experiments
on simulated data show that NLN achieves significant performance on solving
logical equations. Further experiments on real-world data show that NLN
significantly outperforms state-of-the-art models on collaborative filtering
and personalized recommendation tasks.

positive:
The ability to conduct logical reasoning is a fundamental aspect of
intelligent human behavior, and thus an important problem along the way to
human-level artificial intelligence. Traditionally, logic-based symbolic
methods from the field of knowledge representation and reasoning have been used
to equip agents with capabilities that resemble human logical reasoning
qualities. More recently, however, there has been an increasing interest in
using machine learning rather than logic-based symbolic formalisms to tackle
these tasks. In this paper, we employ state-of-the-art methods for training
deep neural networks to devise a novel model that is able to learn how to
effectively perform logical reasoning in the form of basic ontology reasoning.
This is an important and at the same time very natural logical reasoning task,
which is why the presented approach is applicable to a plethora of important
real-world problems. We present the outcomes of several experiments, which show
that our model is able to learn to perform highly accurate ontology reasoning
on very large, diverse, and challenging benchmarks. Furthermore, it turned out
that the suggested approach suffers much less from different obstacles that
prohibit logic-based symbolic reasoning, and, at the same time, is surprisingly
plausible from a biological point of view.

positive:
Analytical reasoning is an essential and challenging task that requires a
system to analyze a scenario involving a set of particular circumstances and
perform reasoning over it to make conclusions. In this paper, we study the
challenge of analytical reasoning of text and introduce a new dataset
consisting of questions from the Law School Admission Test from 1991 to 2016.
We analyze what knowledge understanding and reasoning abilities are required to
do well on this task. Furthermore, to address this reasoning challenge, we
design two different baselines: (1) a Transformer-based method which leverages
the state-of-the-art pre-trained language models and (2) Analytical Reasoning
Machine (ARM), a logical-level reasoning framework extracting symbolic
knowledge (e.g, participants, facts, logical functions) to deduce legitimate
solutions. In our experiments, we find that the Transformer-based models
struggle to solve this task as their performance is close to random guess and
ARM achieves better performance by leveraging symbolic knowledge and
interpretable reasoning steps. Results show that both methods still lag far
behind human performance, which leave further space for future research.

positive:
An important aspect of artificial intelligence (AI) is the ability to reason
in a step-by-step "algorithmic" manner that can be inspected and verified for
its correctness. This is especially important in the domain of question
answering (QA). We argue that the challenge of algorithmic reasoning in QA can
be effectively tackled with a "systems" approach to AI which features a hybrid
use of symbolic and sub-symbolic methods including deep neural networks.
Additionally, we argue that while neural network models with end-to-end
training pipelines perform well in narrow applications such as image
classification and language modelling, they cannot, on their own, successfully
perform algorithmic reasoning, especially if the task spans multiple domains.
We discuss a few notable exceptions and point out how they are still limited
when the QA problem is widened to include other intelligence-requiring tasks.
However, deep learning, and machine learning in general, do play important
roles as components in the reasoning process. We propose an approach to
algorithm reasoning for QA, Deep Algorithmic Question Answering (DAQA), based
on three desirable properties: interpretability, generalizability, and
robustness which such an AI system should possess, and conclude that they are
best achieved with a combination of hybrid and compositional AI.

negative:
Higher-level cognition includes logical reasoning and the ability of question
answering with common sense. The RatioLog project addresses the problem of
rational reasoning in deep question answering by methods from automated
deduction and cognitive computing. In a first phase, we combine techniques from
information retrieval and machine learning to find appropriate answer
candidates from the huge amount of text in the German version of the free
encyclopedia "Wikipedia". In a second phase, an automated theorem prover tries
to verify the answer candidates on the basis of their logical representations.
In a third phase - because the knowledge may be incomplete and inconsistent -,
we consider extensions of logical reasoning to improve the results. In this
context, we work toward the application of techniques from human reasoning: We
employ defeasible reasoning to compare the answers w.r.t. specificity, deontic
logic, normative reasoning, and model construction. Moreover, we use integrated
case-based reasoning and machine learning techniques on the basis of the
semantic structure of the questions and answer candidates to learn giving the
right answers.

negative:
Recently, low-dimensional vector space representations of knowledge graphs
(KGs) have been applied to find answers to conjunctive queries (CQs) over
incomplete KGs. However, the current methods only focus on inductive reasoning,
i.e. answering CQs by predicting facts based on patterns learned from the data,
and lack the ability of deductive reasoning by applying external domain
knowledge. Such (expert or commonsense) domain knowledge is an invaluable
resource which can be used to advance machine intelligence. To address this
shortcoming, we introduce a neural-symbolic method for ontology-mediated CQ
answering over incomplete KGs that operates in the embedding space. More
specifically, we propose various data augmentation strategies to generate
training queries using query-rewriting based methods and then exploit a novel
loss function for training the model. The experimental results demonstrate the
effectiveness of our training strategies and the new loss function, i.e., our
method significantly outperforms the baseline in the settings that require both
inductive and deductive reasoning.

negative:
People readily generalize knowledge to novel domains and stimuli. We present
a theory, instantiated in a computational model, based on the idea that
cross-domain generalization in humans is a case of analogical inference over
structured (i.e., symbolic) relational representations. The model is an
extension of the LISA and DORA models of relational inference and learning. The
resulting model learns both the content and format (i.e., structure) of
relational representations from non-relational inputs without supervision, when
augmented with the capacity for reinforcement learning, leverages these
representations to learn individual domains, and then generalizes to new
domains on the first exposure (i.e., zero-shot learning) via analogical
inference. We demonstrate the capacity of the model to learn structured
relational representations from a variety of simple visual stimuli, and to
perform cross-domain generalization between video games (Breakout and Pong) and
between several psychological tasks. We demonstrate that the model's trajectory
closely mirrors the trajectory of children as they learn about relations,
accounting for phenomena from the literature on the development of children's
reasoning and analogy making. The model's ability to generalize between domains
demonstrates the flexibility afforded by representing domains in terms of their
underlying relational structure, rather than simply in terms of the statistical
relations between their inputs and outputs.

negative:
Recent years have witnessed the success of deep neural networks in many
research areas. The fundamental idea behind the design of most neural networks
is to learn similarity patterns from data for prediction and inference, which
lacks the ability of cognitive reasoning. However, the concrete ability of
reasoning is critical to many theoretical and practical problems. On the other
hand, traditional symbolic reasoning methods do well in making logical
inference, but they are mostly hard rule-based reasoning, which limits their
generalization ability to different tasks since difference tasks may require
different rules. Both reasoning and generalization ability are important for
prediction tasks such as recommender systems, where reasoning provides strong
connection between user history and target items for accurate prediction, and
generalization helps the model to draw a robust user portrait over noisy
inputs.
  In this paper, we propose Logic-Integrated Neural Network (LINN) to integrate
the power of deep learning and logic reasoning. LINN is a dynamic neural
architecture that builds the computational graph according to input logical
expressions. It learns basic logical operations such as AND, OR, NOT as neural
modules, and conducts propositional logical reasoning through the network for
inference. Experiments on theoretical task show that LINN achieves significant
performance on solving logical equations and variables. Furthermore, we test
our approach on the practical task of recommendation by formulating the task
into a logical inference problem. Experiments show that LINN significantly
outperforms state-of-the-art recommendation models in Top-K recommendation,
which verifies the potential of LINN in practice.

negative:
Whether neural networks can learn abstract reasoning or whether they merely
rely on superficial statistics is a topic of recent debate. Here, we propose a
dataset and challenge designed to probe abstract reasoning, inspired by a
well-known human IQ test. To succeed at this challenge, models must cope with
various generalisation `regimes' in which the training and test data differ in
clearly-defined ways. We show that popular models such as ResNets perform
poorly, even when the training and test sets differ only minimally, and we
present a novel architecture, with a structure designed to encourage reasoning,
that does significantly better. When we vary the way in which the test
questions and training data differ, we find that our model is notably
proficient at certain forms of generalisation, but notably weak at others. We
further show that the model's ability to generalise improves markedly if it is
trained to predict symbolic explanations for its answers. Altogether, we
introduce and explore ways to both measure and induce stronger abstract
reasoning in neural networks. Our freely-available dataset should motivate
further progress in this direction.

negative:
we introduce deductive association networks(DANs), a network that performs
deductive reasoning. To have high-dimensional thinking, combining various
axioms and putting the results back into another axiom is necessary to produce
new relationships and results. For example, it would be given two propositions:
"Socrates is a man." and "All men are mortals." and two propositions could be
used to infer the new proposition, "Therefore Socrates is mortal.". To
evaluate, we used MNIST Dataset, a handwritten numerical image dataset, to
apply it to the group theory and show the results of performing deductive
learning.

negative:
We consider three modern roles for logic in artificial intelligence, which
are based on the theory of tractable Boolean circuits: (1) logic as a basis for
computation, (2) logic for learning from a combination of data and knowledge,
and (3) logic for reasoning about the behavior of machine learning systems.

negative:
Deep learning has been shown to achieve impressive results in several tasks
where a large amount of training data is available. However, deep learning
solely focuses on the accuracy of the predictions, neglecting the reasoning
process leading to a decision, which is a major issue in life-critical
applications. Probabilistic logic reasoning allows to exploit both statistical
regularities and specific domain expertise to perform reasoning under
uncertainty, but its scalability and brittle integration with the layers
processing the sensory data have greatly limited its applications. For these
reasons, combining deep architectures and probabilistic logic reasoning is a
fundamental goal towards the development of intelligent agents operating in
complex environments. This paper presents Relational Neural Machines, a novel
framework allowing to jointly train the parameters of the learners and of a
First--Order Logic based reasoner. A Relational Neural Machine is able to
recover both classical learning from supervised data in case of pure
sub-symbolic learning, and Markov Logic Networks in case of pure symbolic
reasoning, while allowing to jointly train and perform inference in hybrid
learning tasks. Proper algorithmic solutions are devised to make learning and
inference tractable in large-scale problems. The experiments show promising
results in different relational tasks.

negative:
Numerical reasoning is often important to accurately understand the world.
Recently, several format-specific datasets have been proposed, such as
numerical reasoning in the settings of Natural Language Inference (NLI),
Reading Comprehension (RC), and Question Answering (QA). Several
format-specific models and architectures in response to those datasets have
also been proposed. However, there exists a strong need for a benchmark which
can evaluate the abilities of models, in performing question format independent
numerical reasoning, as (i) the numerical reasoning capabilities we want to
teach are not controlled by question formats, (ii) for numerical reasoning
technology to have the best possible application, it must be able to process
language and reason in a way that is not exclusive to a single format, task,
dataset or domain. In pursuit of this goal, we introduce NUMBERGAME, a
multifaceted benchmark to evaluate model performance across numerical reasoning
tasks of eight diverse formats. We add four existing question types in our
compilation. Two of the new types we add are about questions that require
external numerical knowledge, commonsense knowledge and domain knowledge. For
building a more practical numerical reasoning system, NUMBERGAME demands four
capabilities beyond numerical reasoning: (i) detecting question format directly
from data (ii) finding intermediate common format to which every format can be
converted (iii) incorporating commonsense knowledge (iv) handling data
imbalance across formats. We build several baselines, including a new model
based on knowledge hunting using a cheatsheet. However, all baselines perform
poorly in contrast to the human baselines, indicating the hardness of our
benchmark. Our work takes forward the recent progress in generic system
development, demonstrating the scope of these under-explored tasks.

negative:
Combining machine learning with logic-based expert systems in order to get
the best of both worlds are becoming increasingly popular. However, to what
extent machine learning can already learn to reason over rule-based knowledge
is still an open problem. In this paper, we explore how symbolic logic, defined
as logic programs at a character level, is learned to be represented in a
high-dimensional vector space using RNN-based iterative neural networks to
perform reasoning. We create a new dataset that defines 12 classes of logic
programs exemplifying increased level of complexity of logical reasoning and
train the networks in an end-to-end fashion to learn whether a logic program
entails a given query. We analyse how learning the inference algorithm gives
rise to representations of atoms, literals and rules within logic programs and
evaluate against increasing lengths of predicate and constant symbols as well
as increasing steps of multi-hop reasoning.

negative:
Recursive neural network models and their accompanying vector representations
for words have seen success in an array of increasingly semantically
sophisticated tasks, but almost nothing is known about their ability to
accurately capture the aspects of linguistic meaning that are necessary for
interpretation or reasoning. To evaluate this, I train a recursive model on a
new corpus of constructed examples of logical reasoning in short sentences,
like the inference of "some animal walks" from "some dog walks" or "some cat
walks," given that dogs and cats are animals. This model learns representations
that generalize well to new types of reasoning pattern in all but a few cases,
a result which is promising for the ability of learned representation models to
capture logical reasoning.

negative:
Deep learning models are widely used for various industrial and scientific
applications. Even though these models have achieved considerable success in
recent years, there exists a lack of understanding of the rationale behind
decisions made by such systems in the machine learning community. This problem
of interpretability is further aggravated by the increasing complexity of such
models. This paper utilizes concepts from machine learning, quantum computation
and quantum field theory to demonstrate how a many valued quantum logic system
naturally arises in a specific class of generative deep learning models called
Convolutional Deep Belief Networks. It provides a robust theoretical framework
for constructing deep learning models equipped with the interpretability of
many valued quantum logic systems without compromising their computing
efficiency.

negative:
Language-guided robots must be able to both ask humans questions and
understand answers. Much existing work focuses only on the latter. In this
paper, we go beyond instruction following and introduce a two-agent task where
one agent navigates and asks questions that a second, guiding agent answers.
Inspired by theory of mind, we propose the Recursive Mental Model (RMM). The
navigating agent models the guiding agent to simulate answers given candidate
generated questions. The guiding agent in turn models the navigating agent to
simulate navigation steps it would take to generate answers. We use the
progress agents make towards the goal as a reinforcement learning reward signal
to directly inform not only navigation actions, but also both question and
answer generation. We demonstrate that RMM enables better generalization to
novel environments. Interlocutor modelling may be a way forward for human-agent
dialogue where robots need to both ask and answer questions.

negative:
Large-scale knowledge graphs provide structured representations of human
knowledge. However, as it is impossible to collect all knowledge, knowledge
graphs are usually incomplete. Reasoning based on existing facts paves a way to
discover missing facts. In this paper, we study the problem of learning logical
rules for reasoning on knowledge graphs for completing missing factual
triplets. Learning logical rules equips a model with strong interpretability as
well as the ability to generalize to similar tasks. We propose a model able to
fully use training data which also considers multi-target scenarios. In
addition, considering the deficiency in evaluating the performance of models
and the quality of mined rules, we further propose two novel indicators to help
with the problem. Experimental results empirically demonstrate that our model
outperforms state-of-the-art methods on five benchmark datasets. The results
also prove the effectiveness of the indicators.

negative:
Semantic Web knowledge representation standards, and in particular RDF and
OWL, often come endowed with a formal semantics which is considered to be of
fundamental importance for the field. Reasoning, i.e., the drawing of logical
inferences from knowledge expressed in such standards, is traditionally based
on logical deductive methods and algorithms which can be proven to be sound and
complete and terminating, i.e. correct in a very strong sense. For various
reasons, though, in particular, the scalability issues arising from the
ever-increasing amounts of Semantic Web data available and the inability of
deductive algorithms to deal with noise in the data, it has been argued that
alternative means of reasoning should be investigated which bear high promise
for high scalability and better robustness. From this perspective, deductive
algorithms can be considered the gold standard regarding correctness against
which alternative methods need to be tested. In this paper, we show that it is
possible to train a Deep Learning system on RDF knowledge graphs, such that it
is able to perform reasoning over new RDF knowledge graphs, with high precision
and recall compared to the deductive gold standard.

negative:
Synthesizing user-intended programs from a small number of input-output
examples is a challenging problem with several important applications like
spreadsheet manipulation, data wrangling and code refactoring. Existing
synthesis systems either completely rely on deductive logic techniques that are
extensively hand-engineered or on purely statistical models that need massive
amounts of data, and in general fail to provide real-time synthesis on
challenging benchmarks. In this work, we propose Neural Guided Deductive Search
(NGDS), a hybrid synthesis technique that combines the best of both symbolic
logic techniques and statistical models. Thus, it produces programs that
satisfy the provided specifications by construction and generalize well on
unseen examples, similar to data-driven systems. Our technique effectively
utilizes the deductive search framework to reduce the learning problem of the
neural component to a simple supervised learning setup. Further, this allows us
to both train on sparingly available real-world data and still leverage
powerful recurrent neural network encoders. We demonstrate the effectiveness of
our method by evaluating on real-world customer scenarios by synthesizing
accurate programs with up to 12x speed-up compared to state-of-the-art systems.

negative:
While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep learning models. For higher-level inference,
however, probabilistic graphical models with their Bayesian nature are still
more powerful and flexible. To achieve integrated intelligence that involves
both perception and inference, it is naturally desirable to tightly integrate
deep learning and Bayesian models within a principled probabilistic framework,
which we call Bayesian deep learning. In this unified framework, the perception
of text or images using deep learning can boost the performance of higher-level
inference and in return, the feedback from the inference process is able to
enhance the perception of text or images. This paper proposes a general
framework for Bayesian deep learning and reviews its recent applications on
recommender systems, topic models, and control. In this paper, we also discuss
the relationship and differences between Bayesian deep learning and other
related topics like Bayesian treatment of neural networks.

negative:
Algorithms of inference in a computer system oriented to input and semantic
processing of text information are presented. Such inference is necessary for
logical questions when the direct comparison of objects from a question and
database can not give a result. The following classes of problems are
considered: a check of hypotheses for persons and non-typical actions, the
determination of persons and circumstances for non-typical actions, planning
actions, the determination of event cause and state of persons. To form an
answer both deduction and plausible reasoning are used. As a knowledge domain
under consideration is social behavior of persons, plausible reasoning is based
on laws of social psychology. Proposed algorithms of inference and plausible
reasoning can be realized in computer systems closely connected with text
processing (criminology, operation of business, medicine, document systems).

negative:
Quantitative reasoning is a higher-order reasoning skill that any intelligent
natural language understanding system can reasonably be expected to handle. We
present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual
Entailment), a new framework for quantitative reasoning in textual entailment.
We benchmark the performance of 9 published NLI models on EQUATE, and find that
on average, state-of-the-art methods do not achieve an absolute improvement
over a majority-class baseline, suggesting that they do not implicitly learn to
reason with quantities. We establish a new baseline Q-REAS that manipulates
quantities symbolically. In comparison to the best performing NLI model, it
achieves success on numerical reasoning tests (+24.2%), but has limited verbal
reasoning capabilities (-8.1%). We hope our evaluation framework will support
the development of models of quantitative reasoning in language understanding.

negative:
In complex inferential tasks like question answering, machine learning models
must confront two challenges: the need to implement a compositional reasoning
process, and, in many applications, the need for this reasoning process to be
interpretable to assist users in both development and prediction. Existing
models designed to produce interpretable traces of their decision-making
process typically require these traces to be supervised at training time. In
this paper, we present a novel neural modular approach that performs
compositional reasoning by automatically inducing a desired sub-task
decomposition without relying on strong supervision. Our model allows linking
different reasoning tasks though shared modules that handle common routines
across tasks. Experiments show that the model is more interpretable to human
evaluators compared to other state-of-the-art models: users can better
understand the model's underlying reasoning procedure and predict when it will
succeed or fail based on observing its intermediate outputs.

negative:
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
  This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
  We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed.

negative:
Exact structured inference with neural network scoring functions is
computationally challenging but several methods have been proposed for
approximating inference. One approach is to perform gradient descent with
respect to the output structure directly (Belanger and McCallum, 2016). Another
approach, proposed recently, is to train a neural network (an "inference
network") to perform inference (Tu and Gimpel, 2018). In this paper, we compare
these two families of inference methods on three sequence labeling datasets. We
choose sequence labeling because it permits us to use exact inference as a
benchmark in terms of speed, accuracy, and search error. Across datasets, we
demonstrate that inference networks achieve a better speed/accuracy/search
error trade-off than gradient descent, while also being faster than exact
inference at similar accuracy levels. We find further benefit by combining
inference networks and gradient descent, using the former to provide a warm
start for the latter.

negative:
Many machine learning applications require the ability to learn from and
reason about noisy multi-relational data. To address this, several effective
representations have been developed that provide both a language for expressing
the structural regularities of a domain, and principled support for
probabilistic inference. In addition to these two aspects, however, many
applications also involve a third aspect-the need to reason about
similarities-which has not been directly supported in existing frameworks. This
paper introduces probabilistic similarity logic (PSL), a general-purpose
framework for joint reasoning about similarity in relational domains that
incorporates probabilistic reasoning about similarities and relational
structure in a principled way. PSL can integrate any existing domain-specific
similarity measures and also supports reasoning about similarities between sets
of entities. We provide efficient inference and learning techniques for PSL and
demonstrate its effectiveness both in common relational tasks and in settings
that require reasoning about similarity.

negative:
Automated reasoning about uncertain knowledge has many applications. One
difficulty when developing such systems is the lack of a completely
satisfactory integration of logic and probability. We address this problem
directly. Expressive languages like higher-order logic are ideally suited for
representing and reasoning about structured knowledge. Uncertain knowledge can
be modeled by using graded probabilities rather than binary truth-values. The
main technical problem studied in this paper is the following: Given a set of
sentences, each having some probability of being true, what probability should
be ascribed to other (query) sentences? A natural wish-list, among others, is
that the probability distribution (i) is consistent with the knowledge base,
(ii) allows for a consistent inference procedure and in particular (iii)
reduces to deductive logic in the limit of probabilities being 0 and 1, (iv)
allows (Bayesian) inductive reasoning and (v) learning in the limit and in
particular (vi) allows confirmation of universally quantified
hypotheses/sentences. We translate this wish-list into technical requirements
for a prior probability and show that probabilities satisfying all our criteria
exist. We also give explicit constructions and several general
characterizations of probabilities that satisfy some or all of the criteria and
various (counter) examples. We also derive necessary and sufficient conditions
for extending beliefs about finitely many sentences to suitable probabilities
over all sentences, and in particular least dogmatic or least biased ones. We
conclude with a brief outlook on how the developed theory might be used and
approximated in autonomous reasoning agents. Our theory is a step towards a
globally consistent and empirically satisfactory unification of probability and
logic.

negative:
The representation of knowledge based on first-order logic captures the
richness of natural language and supports multiple probabilistic inference
models. Although symbolic representation enables quantitative reasoning with
statistical probability, it is difficult to utilize with machine learning
models as they perform numerical operations. In contrast, knowledge embedding
(i.e., high-dimensional and continuous vectors) is a feasible approach to
complex reasoning that can not only retain the semantic information of
knowledge but also establish the quantifiable relationship among them. In this
paper, we propose recursive neural knowledge network (RNKN), which combines
medical knowledge based on first-order logic with recursive neural network for
multi-disease diagnosis. After RNKN is efficiently trained from manually
annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented
knowledge embeddings and weight matrixes are learned. Experimental results
verify that the diagnostic accuracy of RNKN is superior to that of some
classical machine learning models and Markov logic network (MLN). The results
also demonstrate that the more explicit the evidence extracted from CEMRs is,
the better is the performance achieved. RNKN gradually exhibits the
interpretation of knowledge embeddings as the number of training epochs
increases.

negative:
Large knowledge bases (KBs) are useful in many tasks, but it is unclear how
to integrate this sort of knowledge into "deep" gradient-based learning
systems. To address this problem, we describe a probabilistic deductive
database, called TensorLog, in which reasoning uses a differentiable process.
In TensorLog, each clause in a logical theory is first converted into certain
type of factor graph. Then, for each type of query to the factor graph, the
message-passing steps required to perform belief propagation (BP) are
"unrolled" into a function, which is differentiable. We show that these
functions can be composed recursively to perform inference in non-trivial
logical theories containing multiple interrelated clauses and predicates. Both
compilation and inference in TensorLog are efficient: compilation is linear in
theory size and proof depth, and inference is linear in database size and the
number of message-passing steps used in BP. We also present experimental
results with TensorLog and discuss its relationship to other first-order
probabilistic logics.

negative:
In this work, we present a novel approach to ontology reasoning that is based
on deep learning rather than logic-based formal reasoning. To this end, we
introduce a new model for statistical relational learning that is built upon
deep recursive neural networks, and give experimental evidence that it can
easily compete with, or even outperform, existing logic-based reasoners on the
task of ontology reasoning. More precisely, we compared our implemented system
with one of the best logic-based ontology reasoners at present, RDFox, on a
number of large standard benchmark datasets, and found that our system attained
high reasoning quality, while being up to two orders of magnitude faster.

negative:
We propose a framework for inferring the latent attitudes or preferences of
users by performing probabilistic first-order logical reasoning over the social
network graph. Our method answers questions about Twitter users like {\em Does
this user like sushi?} or {\em Is this user a New York Knicks fan?} by building
a probabilistic model that reasons over user attributes (the user's location or
gender) and the social network (the user's friends and spouse), via inferences
like homophily (I am more likely to like sushi if spouse or friends like sushi,
I am more likely to like the Knicks if I live in New York). The algorithm uses
distant supervision, semi-supervised data harvesting and vector space models to
extract user attributes (e.g. spouse, education, location) and preferences
(likes and dislikes) from text. The extracted propositions are then fed into a
probabilistic reasoner (we investigate both Markov Logic and Probabilistic Soft
Logic). Our experiments show that probabilistic logical reasoning significantly
improves the performance on attribute and relation extraction, and also
achieves an F-score of 0.791 at predicting a users likes or dislikes,
significantly better than two strong baselines.

negative:
Probabilistic inference procedures are usually coded painstakingly from
scratch, for each target model and each inference algorithm. We reduce this
effort by generating inference procedures from models automatically. We make
this code generation modular by decomposing inference algorithms into reusable
program-to-program transformations. These transformations perform exact
inference as well as generate probabilistic programs that compute expectations,
densities, and MCMC samples. The resulting inference procedures are about as
accurate and fast as other probabilistic programming systems on real-world
problems.

negative:
Search engines can quickly response a hyperlink list according to query
keywords. However, when a query is complex, developers need to repeatedly
refine the search keywords and open a large number of web pages to find and
summarize answers. Many research works of question and answering (Q and A)
system attempt to assist search engines by providing simple, accurate and
understandable answers. However, without original semantic contexts, these
answers lack explainability, making them difficult for users to trust and
adopt. In this paper, a brain-inspired search engine assistant named
DeveloperBot based on knowledge graph is proposed, which aligns to the
cognitive process of human and has the capacity to answer complex queries with
explainability. Specifically, DeveloperBot firstly constructs a multi-layer
query graph by splitting a complex multi-constraint query into several ordered
constraints. Then it models the constraint reasoning process as subgraph search
process inspired by the spreading activation model of cognitive science. In the
end, novel features of the subgraph will be extracted for decision-making. The
corresponding reasoning subgraph and answer confidence will be derived as
explanations. The results of the decision-making demonstrate that DeveloperBot
can estimate the answers and answer confidences with high accuracy. We
implement a prototype and conduct a user study to evaluate whether and how the
direct answers and the explanations provided by DeveloperBot can assist
developers' information needs.

negative:
Real-world tasks often exhibit a compositional structure that contains a
sequence of simpler sub-tasks. For instance, opening a door requires reaching,
grasping, rotating, and pulling the door knob. Such compositional tasks require
an agent to reason about the sub-task at hand while orchestrating global
behavior accordingly. This can be cast as an online task inference problem,
where the current task identity, represented by a context variable, is
estimated from the agent's past experiences with probabilistic inference.
Previous approaches have employed simple latent distributions, e.g., Gaussian,
to model a single context for the entire task. However, this formulation lacks
the expressiveness to capture the composition and transition of the sub-tasks.
We propose a variational inference framework OCEAN to perform online task
inference for compositional tasks. OCEAN models global and local context
variables in a joint latent space, where the global variables represent a
mixture of sub-tasks required for the task, while the local variables capture
the transitions between the sub-tasks. Our framework supports flexible latent
distributions based on prior knowledge of the task structure and can be trained
in an unsupervised manner. Experimental results show that OCEAN provides more
effective task inference with sequential context adaptation and thus leads to a
performance boost on complex, multi-stage tasks.

negative:
The dynamic, real-time, and accurate inference of model parameters from
empirical data is of great importance in many scientific and engineering
disciplines that use computational models (such as a digital twin) for the
analysis and prediction of complex physical processes. However, fast and
accurate inference for processes with large and high dimensional datasets
cannot easily be achieved with state-of-the-art methods under noisy real-world
conditions. The primary reason is that the inference of model parameters with
traditional techniques based on optimisation or sampling often suffers from
computational and statistical challenges, resulting in a trade-off between
accuracy and deployment time. In this paper, we propose a novel framework for
inference of model parameters based on reinforcement learning. The contribution
of the paper is twofold: 1) We reformulate the inference problem as a tracking
problem with the objective of learning a policy that forces the response of the
physics-based model to follow the observations; 2) We propose the constrained
Lyapunov-based actor-critic (CLAC) algorithm to enable the robust and accurate
inference of physics-based model parameters in real time under noisy real-world
conditions. The proposed methodology is demonstrated and evaluated on two
model-based diagnostics test cases utilizing two different physics-based models
of turbofan engines. The performance of the methodology is compared to that of
two alternative approaches: a state update method (unscented Kalman filter) and
a supervised end-to-end mapping with deep neural networks. The experimental
results demonstrate that the proposed methodology outperforms all other tested
methods in terms of speed and robustness, with high inference accuracy.

negative:
We present the Power Law Graph Transformer, a transformer model with well
defined deductive and inductive tasks for prediction and representation
learning. The deductive task learns the dataset level (global) and instance
level (local) graph structures in terms of learnable power law distribution
parameters. The inductive task outputs the prediction probabilities using the
deductive task output, similar to a transductive model. We trained our model
with Turkish-English and Portuguese-English datasets from TED talk transcripts
for machine translation and compared the model performance and characteristics
to a transformer model with scaled dot product attention trained on the same
experimental setup. We report BLEU scores of $17.79$ and $28.33$ on the
Turkish-English and Portuguese-English translation tasks with our model,
respectively. We also show how a duality between a quantization set and
N-dimensional manifold representation can be leveraged to transform between
local and global deductive-inductive outputs using successive application of
linear and non-linear transformations end-to-end.

negative:
The questions in a crowdsourcing task typically exhibit varying degrees of
difficulty and subjectivity. Their joint effects give rise to the variation in
responses to the same question by different crowd-workers. This variation is
low when the question is easy to answer and objective, and high when it is
difficult and subjective. Unfortunately, current quality control methods for
crowdsourcing consider only the question difficulty to account for the
variation. As a result,these methods cannot distinguish workers personal
preferences for different correct answers of a partially subjective question
from their ability/expertise to avoid objectively wrong answers for that
question. To address this issue, we present a probabilistic model which (i)
explicitly encodes question difficulty as a model parameter and (ii) implicitly
encodes question subjectivity via latent preference factors for crowd-workers.
We show that question subjectivity induces grouping of crowd-workers, revealed
through clustering of their latent preferences. Moreover, we develop a
quantitative measure of the subjectivity of a question. Experiments show that
our model(1) improves the performance of both quality control for crowd-sourced
answers and next answer prediction for crowd-workers,and (2) can potentially
provide coherent rankings of questions in terms of their difficulty and
subjectivity, so that task providers can refine their designs of the
crowdsourcing tasks, e.g. by removing highly subjective questions or
inappropriately difficult questions.

negative:
Our interest in this paper is in meeting a rapidly growing industrial demand
for information extraction from images of documents such as invoices, bills,
receipts etc. In practice users are able to provide a very small number of
example images labeled with the information that needs to be extracted. We
adopt a novel two-level neuro-deductive, approach where (a) we use pre-trained
deep neural networks to populate a relational database with facts about each
document-image; and (b) we use a form of deductive reasoning, related to
meta-interpretive learning of transition systems to learn extraction programs:
Given task-specific transitions defined using the entities and relations
identified by the neural detectors and a small number of instances (usually 1,
sometimes 2) of images and the desired outputs, a resource-bounded
meta-interpreter constructs proofs for the instance(s) via logical deduction; a
set of logic programs that extract each desired entity is easily synthesized
from such proofs. In most cases a single training example together with a
noisy-clone of itself suffices to learn a program-set that generalizes well on
test documents, at which time the value of each entity is determined by a
majority vote across its program-set. We demonstrate our two-level
neuro-deductive approach on publicly available datasets ("Patent" and "Doctor's
Bills") and also describe its use in a real-life industrial problem.

negative:
The Visual Dialogue task requires an agent to engage in a conversation about
an image with a human. It represents an extension of the Visual Question
Answering task in that the agent needs to answer a question about an image, but
it needs to do so in light of the previous dialogue that has taken place. The
key challenge in Visual Dialogue is thus maintaining a consistent, and natural
dialogue while continuing to answer questions correctly. We present a novel
approach that combines Reinforcement Learning and Generative Adversarial
Networks (GANs) to generate more human-like responses to questions. The GAN
helps overcome the relative paucity of training data, and the tendency of the
typical MLE-based approach to generate overly terse answers. Critically, the
GAN is tightly integrated into the attention mechanism that generates
human-interpretable reasons for each answer. This means that the discriminative
model of the GAN has the task of assessing whether a candidate answer is
generated by a human or not, given the provided reason. This is significant
because it drives the generative model to produce high quality answers that are
well supported by the associated reasoning. The method also generates the
state-of-the-art results on the primary benchmark.

negative:
Answering open-ended questions is an essential capability for any intelligent
agent. One of the most interesting recent open-ended question answering
challenges is Visual Question Answering (VQA) which attempts to evaluate a
system's visual understanding through its answers to natural language questions
about images. There exist many approaches to VQA, the majority of which do not
exhibit deeper semantic understanding of the candidate answers they produce. We
study the importance of generating plausible answers to a given question by
introducing the novel task of `Answer Proposal': for a given open-ended
question, a system should generate a ranked list of candidate answers informed
by the semantics of the question. We experiment with various models including a
neural generative model as well as a semantic graph matching one. We provide
both intrinsic and extrinsic evaluations for the task of Answer Proposal,
showing that our best model learns to propose plausible answers with a high
recall and performs competitively with some other solutions to VQA.

negative:
The black-box nature of neural models has motivated a line of research that
aims to generate natural language rationales to explain why a model made
certain predictions. Such rationale generation models, to date, have been
trained on dataset-specific crowdsourced rationales, but this approach is
costly and is not generalizable to new tasks and domains. In this paper, we
investigate the extent to which neural models can reason about natural language
rationales that explain model predictions, relying only on distant supervision
with no additional annotation cost for human-written rationales. We investigate
multiple ways to automatically generate rationales using pre-trained language
models, neural knowledge models, and distant supervision from related tasks,
and train generative models capable of composing explanatory rationales for
unseen instances. We demonstrate our approach on the defeasible inference task,
a nonmonotonic reasoning task in which an inference may be strengthened or
weakened when new information (an update) is introduced. Our model shows
promises at generating post-hoc rationales explaining why an inference is more
or less likely given the additional information, however, it mostly generates
trivial rationales reflecting the fundamental limitations of neural language
models. Conversely, the more realistic setup of jointly predicting the update
or its type and generating rationale is more challenging, suggesting an
important future direction.

negative:
This paper makes a first step towards a logic of learning from experiments.
For this, we investigate formal frameworks for modeling the interaction of
causal and (qualitative) epistemic reasoning. Crucial for our approach is the
idea that the notion of an intervention can be used as a formal expression of a
(real or hypothetical) experiment. In a first step we extend the well-known
causal models with a simple Hintikka-style representation of the epistemic
state of an agent. In the resulting setting, one can talk not only about the
knowledge of an agent about the values of variables and how interventions
affect them, but also about knowledge update. The resulting logic can model
reasoning about thought experiments. However, it is unable to account for
learning from experiments, which is clearly brought out by the fact that it
validates the no learning principle for interventions. Therefore, in a second
step, we implement a more complex notion of knowledge that allows an agent to
observe (measure) certain variables when an experiment is carried out. This
extended system does allow for learning from experiments. For all the proposed
logical systems, we provide a sound and complete axiomatization.

negative:
Reasoning and inference are central to human and artificial intelligence.
Modeling inference in human language is very challenging. With the availability
of large annotated data (Bowman et al., 2015), it has recently become feasible
to train neural network based inference models, which have shown to be very
effective. In this paper, we present a new state-of-the-art result, achieving
the accuracy of 88.6% on the Stanford Natural Language Inference Dataset.
Unlike the previous top models that use very complicated network architectures,
we first demonstrate that carefully designing sequential inference models based
on chain LSTMs can outperform all previous models. Based on this, we further
show that by explicitly considering recursive architectures in both local
inference modeling and inference composition, we achieve additional
improvement. Particularly, incorporating syntactic parsing information
contributes to our best result---it further improves the performance even when
added to the already very strong model.

negative:
The tension between deduction and induction is perhaps the most fundamental
issue in areas such as philosophy, cognition and artificial intelligence (AI).
The deduction camp concerns itself with questions about the expressiveness of
formal languages for capturing knowledge about the world, together with proof
systems for reasoning from such knowledge bases. The learning camp attempts to
generalize from examples about partial descriptions about the world. In AI,
historically, these camps have loosely divided the development of the field,
but advances in cross-over areas such as statistical relational learning,
neuro-symbolic systems, and high-level control have illustrated that the
dichotomy is not very constructive, and perhaps even ill-formed. In this
article, we survey work that provides further evidence for the connections
between logic and learning. Our narrative is structured in terms of three
strands: logic versus learning, machine learning for logic, and logic for
machine learning, but naturally, there is considerable overlap. We place an
emphasis on the following "sore" point: there is a common misconception that
logic is for discrete properties, whereas probability theory and machine
learning, more generally, is for continuous properties. We report on results
that challenge this view on the limitations of logic, and expose the role that
logic can play for learning in infinite domains.

negative:
We fine-tune GPT-3 to answer long-form questions using a text-based
web-browsing environment, which allows the model to search and navigate the
web. By setting up the task so that it can be performed by humans, we are able
to train models on the task using imitation learning, and then optimize answer
quality with human feedback. To make human evaluation of factual accuracy
easier, models must collect references while browsing in support of their
answers. We train and evaluate our models on ELI5, a dataset of questions asked
by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior
cloning, and then performing rejection sampling against a reward model trained
to predict human preferences. This model's answers are preferred by humans 56%
of the time to those of our human demonstrators, and 69% of the time to the
highest-voted answer from Reddit.

negative:
Inference and decision making under uncertainty are key processes in every
autonomous system and numerous robotic problems. In recent years, the
similarities between inference and decision making triggered much work, from
developing unified computational frameworks to pondering about the duality
between the two. In spite of these efforts, inference and control, as well as
inference and belief space planning (BSP) are still treated as two separate
processes. In this paper we propose a paradigm shift, a novel approach which
deviates from conventional Bayesian inference and utilizes the similarities
between inference and BSP. We make the key observation that inference can be
efficiently updated using predictions made during the decision making stage,
even in light of inconsistent data association between the two. We developed a
two staged process that implements our novel approach and updates inference
using calculations from the precursory planning phase. Using autonomous
navigation in an unknown environment along with iSAM2 efficient methodologies
as a test case, we benchmarked our novel approach against standard Bayesian
inference, both with synthetic and real-world data (KITTI dataset). Results
indicate that not only our approach improves running time by at least a factor
of two while providing the same estimation accuracy, but it also alleviates the
computational burden of state dimensionality and loop closures.

negative:
Logic reasoning is a significant ability of human intelligence and also an
important task in artificial intelligence. The existing logic reasoning
methods, quite often, need to design some reasoning patterns beforehand. This
has led to an interesting question: can logic reasoning patterns be directly
learned from given data? The problem is termed as a data concept logic. In this
study, a learning logic task from images, called a LiLi task, first is
proposed. This task is to learn and reason the logic relation from images,
without presetting any reasoning patterns. As a preliminary exploration, we
design six LiLi data sets (Bitwise And, Bitwise Or, Bitwise Xor, Addition,
Subtraction and Multiplication), in which each image is embedded with a n-digit
number. It is worth noting that a learning model beforehand does not know the
meaning of the n-digit numbers embedded in images and the relation between the
input images and the output image. In order to tackle the task, in this work we
use many typical neural network models and produce fruitful results. However,
these models have the poor performances on the difficult logic task. For
furthermore addressing this task, a novel network framework called a divide and
conquer model by adding some label information is designed, achieving a high
testing accuracy.

negative:
We explore a quantitative interpretation of 2-dimensional intuitionistic type
theory (ITT) in which the identity type is interpreted as a "type of
differences". We show that a fragment of ITT, that we call difference type
theory (dTT), yields a general logical framework to talk about quantitative
properties of programs like approximate equivalence and metric preservation. To
demonstrate this fact, we show that dTT can be used to capture compositional
reasoning in presence of errors, since any program can be associated with a
"derivative" relating errors in input with errors in output. Moreover, after
relating the semantics of dTT to the standard weak factorization systems
semantics of ITT, we describe the interpretation of dTT in some quantitative
models developed for approximate program transformations, incremental
computing, program differentiation and differential privacy.

negative:
Many intellectual endeavors require mathematical problem solving, but this
skill remains beyond the capabilities of computers. To measure this ability in
machine learning models, we introduce MATH, a new dataset of 12,500 challenging
competition mathematics problems. Each problem in MATH has a full step-by-step
solution which can be used to teach models to generate answer derivations and
explanations. To facilitate future research and increase accuracy on MATH, we
also contribute a large auxiliary pretraining dataset which helps teach models
the fundamentals of mathematics. Even though we are able to increase accuracy
on MATH, our results show that accuracy remains relatively low, even with
enormous Transformer models. Moreover, we find that simply increasing budgets
and model parameter counts will be impractical for achieving strong
mathematical reasoning if scaling trends continue. While scaling Transformers
is automatically solving most other text-based tasks, scaling is not currently
solving MATH. To have more traction on mathematical problem solving we will
likely need new algorithmic advancements from the broader research community.

negative:
This paper presents a novel method to generate answers for non-extraction
machine reading comprehension (MRC) tasks whose answers cannot be simply
extracted as one span from the given passages. Using a pointer network-style
extractive decoder for such type of MRC may result in unsatisfactory
performance when the ground-truth answers are given by human annotators or
highly re-paraphrased from parts of the passages. On the other hand, using
generative decoder cannot well guarantee the resulted answers with well-formed
syntax and semantics when encountering long sentences. Therefore, to alleviate
the obvious drawbacks of both sides, we propose an answer making-up method from
extracted multi-spans that are learned by our model as highly confident
$n$-gram candidates in the given passage. That is, the returned answers are
composed of discontinuous multi-spans but not just one consecutive span in the
given passages anymore. The proposed method is simple but effective: empirical
experiments on MS MARCO show that the proposed method has a better performance
on accurately generating long answers, and substantially outperforms two
competitive typical one-span and Seq2Seq baseline decoders.

negative:
The analysis of practical probabilistic models on the computer demands a
convenient representation for the available knowledge and an efficient
algorithm to perform inference. An appealing representation is the influence
diagram, a network that makes explicit the random variables in a model and
their probabilistic dependencies. Recent advances have developed solution
procedures based on the influence diagram. In this paper, we examine the
fundamental properties that underlie those techniques, and the information
about the probabilistic structure that is available in the influence diagram
representation. The influence diagram is a convenient representation for
computer processing while also being clear and non-mathematical. It displays
probabilistic dependence precisely, in a way that is intuitive for decision
makers and experts to understand and communicate. As a result, the same
influence diagram can be used to build, assess and analyze a model,
facilitating changes in the formulation and feedback from sensitivity analysis.
The goal in this paper is to determine arbitrary conditional probability
distributions from a given probabilistic model. Given qualitative information
about the dependence of the random variables in the model we can, for a
specific conditional expression, specify precisely what quantitative
information we need to be able to determine the desired conditional probability
distribution. It is also shown how we can find that probability distribution by
performing operations locally, that is, over subspaces of the joint
distribution. In this way, we can exploit the conditional independence present
in the model to avoid having to construct or manipulate the full joint
distribution. These results are extended to include maximal processing when the
information available is incomplete, and optimal decision making in an
uncertain environment. Influence diagrams as a computer-aided modeling tool
were developed by Miller, Merkofer, and Howard [5] and extended by Howard and
Matheson [2]. Good descriptions of how to use them in modeling are in Owen [7]
and Howard and Matheson [2]. The notion of solving a decision problem through
influence diagrams was examined by Olmsted [6] and such an algorithm was
developed by Shachter [8]. The latter paper also shows how influence diagrams
can be used to perform a variety of sensitivity analyses. This paper extends
those results by developing a theory of the properties of the diagram that are
used by the algorithm, and the information needed to solve arbitrary
probability inference problems. Section 2 develops the notation and the
framework for the paper and the relationship between influence diagrams and
joint probability distributions. The general probabilistic inference problem is
posed in Section 3. In Section 4 the transformations on the diagram are
developed and then put together into a solution procedure in Section 5. In
Section 6, this procedure is used to calculate the information requirement to
solve an inference problem and the maximal processing that can be performed
with incomplete information. Section 7 contains a summary of results.

negative:
Self/other distinction and self-recognition are important skills for
interacting with the world, as it allows humans to differentiate own actions
from others and be self-aware. However, only a selected group of animals,
mainly high order mammals such as humans, has passed the mirror test, a
behavioural experiment proposed to assess self-recognition abilities. In this
paper, we describe self-recognition as a process that is built on top of body
perception unconscious mechanisms. We present an algorithm that enables a robot
to perform non-appearance self-recognition on a mirror and distinguish its
simple actions from other entities, by answering the following question: am I
generating these sensations? The algorithm combines active inference, a
theoretical model of perception and action in the brain, with neural network
learning. The robot learns the relation between its actions and its body with
the effect produced in the visual field and its body sensors. The prediction
error generated between the models and the real observations during the
interaction is used to infer the body configuration through free energy
minimization and to accumulate evidence for recognizing its body. Experimental
results on a humanoid robot show the reliability of the algorithm for different
initial conditions, such as mirror recognition in any perspective, robot-robot
distinction and human-robot differentiation.

negative:
The use of deductive techniques, such as theorem provers, has several
advantages in safety verification of hybrid sys- tems; however,
state-of-the-art theorem provers require ex- tensive manual intervention.
Furthermore, there is often a gap between the type of assistance that a theorem
prover requires to make progress on a proof task and the assis- tance that a
system designer is able to provide. This paper presents an extension to
KeYmaera, a deductive verification tool for differential dynamic logic; the new
technique allows local reasoning using system designer intuition about per-
formance within particular modes as part of a proof task. Our approach allows
the theorem prover to leverage for- ward invariants, discovered using numerical
techniques, as part of a proof of safety. We introduce a new inference rule
into the proof calculus of KeYmaera, the forward invariant cut rule, and we
present a methodology to discover useful forward invariants, which are then
used with the new cut rule to complete verification tasks. We demonstrate how
our new approach can be used to complete verification tasks that lie out of the
reach of existing deductive approaches us- ing several examples, including one
involving an automotive powertrain control system.

negative:
Most software verification tools can be classified into one of a number of
established families, each of which has their own focus and strengths. For
example, concrete counterexample generation in model checking, invariant
inference in abstract interpretation and completeness via annotation for
deductive verification. This creates a significant and fundamental usability
problem as users may have to learn and use one technique to find potential
problems but then need an entirely different one to show that they have been
fixed. This paper presents a single, unified algorithm kIkI, which strictly
generalises abstract interpretation, bounded model checking and k-induction.
This not only combines the strengths of these techniques but allows them to
interact and reinforce each other, giving a `single-tool' approach to
verification.

negative:
Conversational and task-oriented dialogue systems aim to interact with the
user using natural responses through multi-modal interfaces, such as text or
speech. These desired responses are in the form of full-length natural answers
generated over facts retrieved from a knowledge source. While the task of
generating natural answers to questions from an answer span has been widely
studied, there has been little research on natural sentence generation over
spoken content. We propose a novel system to generate full length natural
language answers from spoken questions and factoid answers. The spoken sequence
is compactly represented as a confusion network extracted from a pre-trained
Automatic Speech Recognizer. This is the first attempt towards generating
full-length natural answers from a graph input(confusion network) to the best
of our knowledge. We release a large-scale dataset of 259,788 samples of spoken
questions, their factoid answers and corresponding full-length textual answers.
Following our proposed approach, we achieve comparable performance with best
ASR hypothesis.

negative:
Objective: We investigate whether deep learning techniques for natural
language processing (NLP) can be used efficiently for patient phenotyping.
Patient phenotyping is a classification task for determining whether a patient
has a medical condition, and is a crucial part of secondary analysis of
healthcare data. We assess the performance of deep learning algorithms and
compare them with classical NLP approaches.
  Materials and Methods: We compare convolutional neural networks (CNNs),
n-gram models, and approaches based on cTAKES that extract pre-defined medical
concepts from clinical notes and use them to predict patient phenotypes. The
performance is tested on 10 different phenotyping tasks using 1,610 discharge
summaries extracted from the MIMIC-III database.
  Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The
average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our
model having an F1-score up to 37 points higher than alternative approaches. We
additionally assess the interpretability of our model by presenting a method
that extracts the most salient phrases for a particular prediction.
  Conclusion: We show that NLP methods based on deep learning improve the
performance of patient phenotyping. Our CNN-based algorithm automatically
learns the phrases associated with each patient phenotype. As such, it reduces
the annotation complexity for clinical domain experts, who are normally
required to develop task-specific annotation rules and identify relevant
phrases. Our method performs well in terms of both performance and
interpretability, which indicates that deep learning is an effective approach
to patient phenotyping based on clinicians' notes.

negative:
Question answering is an important and difficult task in the natural language
processing domain, because many basic natural language processing tasks can be
cast into a question answering task. Several deep neural network architectures
have been developed recently, which employ memory and inference components to
memorize and reason over text information, and generate answers to questions.
However, a major drawback of many such models is that they are capable of only
generating single-word answers. In addition, they require large amount of
training data to generate accurate answers. In this paper, we introduce the
Long-Term Memory Network (LTMN), which incorporates both an external memory
module and a Long Short-Term Memory (LSTM) module to comprehend the input data
and generate multi-word answers. The LTMN model can be trained end-to-end using
back-propagation and requires minimal supervision. We test our model on two
synthetic data sets (based on Facebook's bAbI data set) and the real-world
Stanford question answering data set, and show that it can achieve
state-of-the-art performance.

negative:
Tackling Natural Language Inference with a logic-based method is becoming
less and less common. While this might have been counterintuitive several
decades ago, nowadays it seems pretty obvious. The main reasons for such a
conception are that (a) logic-based methods are usually brittle when it comes
to processing wide-coverage texts, and (b) instead of automatically learning
from data, they require much of manual effort for development. We make a step
towards to overcome such shortcomings by modeling learning from data as
abduction: reversing a theorem-proving procedure to abduce semantic relations
that serve as the best explanation for the gold label of an inference problem.
In other words, instead of proving sentence-level inference relations with the
help of lexical relations, the lexical relations are proved taking into account
the sentence-level inference relations. We implement the learning method in a
tableau theorem prover for natural language and show that it improves the
performance of the theorem prover on the SICK dataset by 1.4% while still
maintaining high precision (>94%). The obtained results are competitive with
the state of the art among logic-based systems.

negative:
Natural Language Inference (NLI) is the task of determining the semantic
relationship between a premise and a hypothesis. In this paper, we focus on the
{\em generation} of hypotheses from premises in a multimodal setting, to
generate a sentence (hypothesis) given an image and/or its description
(premise) as the input. The main goals of this paper are (a) to investigate
whether it is reasonable to frame NLI as a generation task; and (b) to consider
the degree to which grounding textual premises in visual information is
beneficial to generation. We compare different neural architectures, showing
through automatic and human evaluation that entailments can indeed be generated
successfully. We also show that multimodal models outperform unimodal models in
this task, albeit marginally.

negative:
As the interest in Artificial Intelligence continues to grow it is becoming
more and more important to investigate formalization and tools that allow us to
exploit logic to reason about the world. In particular, given the increasing
number of multi-agents systems that could benefit from techniques of automated
reasoning, exploring new ways to define not only the world's status but also
the agents' information is constantly growing in importance. This type of
reasoning, i.e., about agents' perception of the world and also about agents'
knowledge of her and others' knowledge, is referred to as epistemic reasoning.
  In our work we will try to formalize this concept, expressed through
epistemic logic, for dynamic domains. In particular we will attempt to define a
new action-based language for multi-agent epistemic planning and to implement
an epistemic planner based on it. This solver should provide a tool flexible
enough to be able to reason on different domains, e.g., economy, security,
justice and politics, where reasoning about others' beliefs could lead to
winning strategies or help in changing a group of agents' view of the world.

negative:
Owing to the advancement of deep learning, artificial systems are now rival
to humans in several pattern recognition tasks, such as visual recognition of
object categories. However, this is only the case with the tasks for which
correct answers exist independent of human perception. There is another type of
tasks for which what to predict is human perception itself, in which there are
often individual differences. Then, there are no longer single "correct"
answers to predict, which makes evaluation of artificial systems difficult. In
this paper, focusing on pairwise ranking tasks sensitive to individual
differences, we propose an evaluation method. Given a ranking result for
multiple item pairs that is generated by an artificial system, our method
quantifies the probability that the same ranking result will be generated by
humans, and judges if it is distinguishable from human-generated results. We
introduce a probabilistic model of human ranking behavior, and present an
efficient computation method for the judgment. To estimate model parameters
accurately from small-size samples, we present a method that uses confidence
scores given by annotators for ranking each item pair. Taking as an example a
task of ranking image pairs according to material attributes of objects, we
demonstrate how the proposed method works.

negative:
This paper tackles the goal of conclusion-supplement answer generation for
non-factoid questions, which is a critical issue in the field of Natural
Language Processing (NLP) and Artificial Intelligence (AI), as users often
require supplementary information before accepting a conclusion. The current
encoder-decoder framework, however, has difficulty generating such answers,
since it may become confused when it tries to learn several different long
answers to the same non-factoid question. Our solution, called an ensemble
network, goes beyond single short sentences and fuses logically connected
conclusion statements and supplementary statements. It extracts the context
from the conclusion decoder's output sequence and uses it to create
supplementary decoder states on the basis of an attention mechanism. It also
assesses the closeness of the question encoder's output sequence and the
separate outputs of the conclusion and supplement decoders as well as their
combination. As a result, it generates answers that match the questions and
have natural-sounding supplementary sequences in line with the context
expressed by the conclusion sequence. Evaluations conducted on datasets
including "Love Advice" and "Arts & Humanities" categories indicate that our
model outputs much more accurate results than the tested baseline models do.

negative:
The most important problems for society are describable only in vague terms,
dependent on subjective positions, and missing highly relevant data. This
thesis is intended to revive and further develop the view that giving
non-trivial, rigorous deductive arguments concerning such problems -without
eliminating the complications of vagueness, subjectivity, and uncertainty- is,
though very difficult, not problematic in principle, does not require the
invention of new logics -classical first-order logic will do- and is something
that more mathematically-inclined people should be pursuing. The framework of
interpreted formal proofs is presented for formalizing and criticizing rigorous
deductive arguments about vague, subjective, and uncertain issues, and its
adequacy is supported largely by a number of major examples. This thesis also
documents progress towards a web system for collaboratively authoring and
criticizing such arguments, which is the ultimate goal of this project.

negative:
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In order to apply the rationality result of belief dynamics theory to various
practical problems, it should be generalized in two respects: first it should
allow a certain part of belief to be declared as immutable; and second, the
belief state need not be deductively closed. Such a generalization of belief
dynamics, referred to as base dynamics, is presented in this paper, along with
the concept of a generalized revision algorithm for knowledge bases (Horn or
Horn logic with stratified negation). We show that knowledge base dynamics has
an interesting connection with kernel change via hitting set and abduction. In
this paper, we show how techniques from disjunctive logic programming can be
used for efficient (deductive) database updates. The key idea is to transform
the given database together with the update request into a disjunctive
(datalog) logic program and apply disjunctive techniques (such as minimal model
reasoning) to solve the original update problem. The approach extends and
integrates standard techniques for efficient query answering and integrity
checking. The generation of a hitting set is carried out through a hyper
tableaux calculus and magic set that is focused on the goal of minimality.

negative:
Visual Question Answering is a multi-modal task that aims to measure
high-level visual understanding. Contemporary VQA models are restrictive in the
sense that answers are obtained via classification over a limited vocabulary
(in the case of open-ended VQA), or via classification over a set of
multiple-choice-type answers. In this work, we present a completely generative
formulation where a multi-word answer is generated for a visual query. To take
this a step forward, we introduce a new task: ViQAR (Visual Question Answering
and Reasoning), wherein a model must generate the complete answer and a
rationale that seeks to justify the generated answer. We propose an end-to-end
architecture to solve this task and describe how to evaluate it. We show that
our model generates strong answers and rationales through qualitative and
quantitative evaluation, as well as through a human Turing Test.

negative:
For a computational system to be intelligent, it should be able to perform,
at least, basic deductions. Nonetheless, since deductions are, in some sense,
equivalent to tautologies, it seems that they do not provide new information.
The present article proposes a measure the degree of semantic informativity of
valid deductions in a dynamic setting. Concepts of coherency and relevancy,
displayed in terms of insertions and deletions on databases, are used to define
semantic informativity. In this way, the article shows that a solution to the
problem about the informativity of deductions provides a heuristic principle to
improve the deductive power of computational systems.

negative:
We explore the problem of step-wise explaining how to solve constraint
satisfaction problems, with a use case on logic grid puzzles. More
specifically, we study the problem of explaining the inference steps that one
can take during propagation, in a way that is easy to interpret for a person.
Thereby, we aim to give the constraint solver explainable agency, which can
help in building trust in the solver by being able to understand and even learn
from the explanations. The main challenge is that of finding a sequence of
simple explanations, where each explanation should aim to be as cognitively
easy as possible for a human to verify and understand. This contrasts with the
arbitrary combination of facts and constraints that the solver may use when
propagating. We propose the use of a cost function to quantify how simple an
individual explanation of an inference step is, and identify the
explanation-production problem of finding the best sequence of explanations of
a CSP. Our approach is agnostic of the underlying constraint propagation
mechanisms, and can provide explanations even for inference steps resulting
from combinations of constraints. In case multiple constraints are involved, we
also develop a mechanism that allows to break the most difficult steps up and
thus gives the user the ability to zoom in on specific parts of the
explanation. Our proposed algorithm iteratively constructs the explanation
sequence by using an optimistic estimate of the cost function to guide the
search for the best explanation at each step. Our experiments on logic grid
puzzles show the feasibility of the approach in terms of the quality of the
individual explanations and the resulting explanation sequences obtained.

negative:
(l) I have enough evidence to render the sentence S probable. (la) So,
relative to what I know, it is rational of me to believe S. (2) Now that I have
more evidence, S may no longer be probable. (2a) So now, relative to what I
know, it is not rational of me to believe S. These seem a perfectly ordinary,
common sense, pair of situations. Generally and vaguely, I take them to embody
what I shall call probabilistic inference. This form of inference is clearly
non-monotonic. Relatively few people have taken this form of inference, based
on high probability, to serve as a foundation for non-monotonic logic or for a
logical or defeasible inference. There are exceptions: Jane Nutter [16] thinks
that sometimes probability has something to do with non-monotonic reasoning.
Judea Pearl [ 17] has recently been exploring the possibility. There are any
number of people whom one might call probability enthusiasts who feel that
probability provides all the answers by itself, with no need of help from
logic. Cheeseman [1], Henrion [5] and others think it useful to look at a
distribution of probabilities over a whole algebra of statements, to update
that distribution in the light of new evidence, and to use the latest updated
distribution of probability over the algebra as a basis for planning and
decision making. A slightly weaker form of this approach is captured by Nilsson
[15], where one assumes certain probabilities for certain statements, and
infers the probabilities, or constraints on the probabilities of other
statement. None of this corresponds to what I call probabilistic inference. All
of the inference that is taking place, either in Bayesian updating, or in
probabilistic logic, is strictly deductive. Deductive inference, particularly
that concerned with the distribution of classical probabilities or chances, is
of great importance. But this is not to say that there is no important role for
what earlier logicians have called "ampliative" or "inductive" or "scientific"
inference, in which the conclusion goes beyond the premises, asserts more than
do the premises. This depends on what David Israel [6] has called "real rules
of inference". It is characteristic of any such logic or inference procedure
that it can go wrong: that statements accepted at one point may be rejected at
a later point. Research underlying the results reported here has been partially
supported by the Signals Warfare Center of the United States Army.

negative:
We present a unified logical framework for representing and reasoning about
both probability quantitative and qualitative preferences in probability answer
set programming, called probability answer set optimization programs. The
proposed framework is vital to allow defining probability quantitative
preferences over the possible outcomes of qualitative preferences. We show the
application of probability answer set optimization programs to a variant of the
well-known nurse restoring problem, called the nurse restoring with probability
preferences problem. To the best of our knowledge, this development is the
first to consider a logical framework for reasoning about probability
quantitative preferences, in general, and reasoning about both probability
quantitative and qualitative preferences in particular.

negative:
We present a unified logical framework for representing and reasoning about
both quantitative and qualitative preferences in fuzzy answer set programming,
called fuzzy answer set optimization programs. The proposed framework is vital
to allow defining quantitative preferences over the possible outcomes of
qualitative preferences. We show the application of fuzzy answer set
optimization programs to the course scheduling with fuzzy preferences problem.
To the best of our knowledge, this development is the first to consider a
logical framework for reasoning about quantitative preferences, in general, and
reasoning about both quantitative and qualitative preferences in particular.

negative:
Deep inference is a proof theoretic methodology that generalizes the standard
notion of inference of the sequent calculus, whereby inference rules become
applicable at any depth inside logical expressions. Deep inference provides
more freedom in the design of deductive systems for different logics and a rich
combinatoric analysis of proofs. In particular, construction of exponentially
shorter analytic proofs becomes possible, however with the cost of a greater
nondeterminism than in the sequent calculus. In this paper, we show that the
nondeterminism in proof search can be reduced without losing the shorter proofs
and without sacrificing proof theoretic cleanliness. For this, we exploit an
interaction and depth scheme in the logical expressions. We demonstrate our
method on deep inference systems for multiplicative linear logic and classical
logic, discuss its proof complexity and its relation to focusing, and present
implementations.

negative:
Recently, FO(C), the integration of C-Log with classical logic, was
introduced as a knowledge representation language. Up to this point, no systems
exist that perform inference on FO(C), and very little is known about
properties of inference in FO(C). In this paper, we study both of the above
problems. We define normal forms for FO(C), one of which corresponds to FO(ID).
We define transformations between these normal forms, and show that, using
these transformations, several inference tasks for FO(C) can be reduced to
inference tasks for FO(ID), for which solvers exist. We implemented a prototype
of this transformation, and thus present the first system to perform inference
in FO(C). We also provide results about the complexity of reasoning in FO(C).

negative:
We propose a benchmark to measure whether a language model is truthful in
generating answers to questions. The benchmark comprises 817 questions that
span 38 categories, including health, law, finance and politics. We crafted
questions that some humans would answer falsely due to a false belief or
misconception. To perform well, models must avoid generating false answers
learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a
T5-based model. The best model was truthful on 58% of questions, while human
performance was 94%. Models generated many false answers that mimic popular
misconceptions and have the potential to deceive humans. The largest models
were generally the least truthful. This contrasts with other NLP tasks, where
performance improves with model size. However, this result is expected if false
answers are learned from the training distribution. We suggest that scaling up
models alone is less promising for improving truthfulness than fine-tuning
using training objectives other than imitation of text from the web.

negative:
Arriving at the complete probabilistic knowledge of a domain, i.e., learning
how all variables interact, is indeed a demanding task. In reality, settings
often arise for which an individual merely possesses partial knowledge of the
domain, and yet, is expected to give adequate answers to a variety of posed
queries. That is, although precise answers to some queries, in principle,
cannot be achieved, a range of plausible answers is attainable for each query
given the available partial knowledge. In this paper, we propose the
Multi-Context Model (MCM), a new graphical model to represent the state of
partial knowledge as to a domain. MCM is a middle ground between Probabilistic
Logic, Bayesian Logic, and Probabilistic Graphical Models. For this model we
discuss: (i) the dynamics of constructing a contradiction-free MCM, i.e., to
form partial beliefs regarding a domain in a gradual and probabilistically
consistent way, and (ii) how to perform inference, i.e., to evaluate a
probability of interest involving some variables of the domain.

negative:
It is well known that reinforcement learning can be cast as inference in an
appropriate probabilistic model. However, this commonly involves introducing a
distribution over agent trajectories with probabilities proportional to
exponentiated rewards. In this work, we formulate reinforcement learning as
Bayesian inference without resorting to rewards, and show that rewards are
derived from agent's preferences, rather than the other way around. We argue
that agent preferences should be specified stochastically rather than
deterministically. Reinforcement learning via inference with stochastic
preferences naturally describes agent behaviors, does not require introducing
rewards and exponential weighing of trajectories, and allows to reason about
agents using the solid foundation of Bayesian statistics. Stochastic
conditioning, a probabilistic programming paradigm for conditioning models on
distributions rather than values, is the formalism behind agents with
probabilistic preferences. We demonstrate realization of our approach on case
studies using both a two-agent coordinate game and a single agent acting in a
noisy environment, showing that despite superficial differences, both cases can
be modeled and reasoned about based on the same principles.

negative:
Many algorithms and applications involve repeatedly solving variations of the
same inference problem; for example we may want to introduce new evidence to
the model or perform updates to conditional dependencies. The goal of adaptive
inference is to take advantage of what is preserved in the model and perform
inference more rapidly than from scratch. In this paper, we describe techniques
for adaptive inference on general graphs that support marginal computation and
updates to the conditional probabilities and dependencies in logarithmic time.
We give experimental results for an implementation of our algorithm, and
demonstrate its potential performance benefit in the study of protein
structure.

negative:
Planning is useful. It lets people take actions that have desirable long-term
consequences. But, planning is hard. It requires thinking about consequences,
which consumes limited computational and cognitive resources. Thus, people
should plan their actions, but they should also be smart about how they deploy
resources used for planning their actions. Put another way, people should also
"plan their plans". Here, we formulate this aspect of planning as a
meta-reasoning problem and formalize it in terms of a recursive Bellman
objective that incorporates both task rewards and information-theoretic
planning costs. Our account makes quantitative predictions about how people
should plan and meta-plan as a function of the overall structure of a task,
which we test in two experiments with human participants. We find that people's
reaction times reflect a planned use of information processing, consistent with
our account. This formulation of planning to plan provides new insight into the
function of hierarchical planning, state abstraction, and cognitive control in
both humans and machines.

negative:
This paper concerns the relation between process algebra and Hoare logic. We
investigate the question whether and how a Hoare logic can be used for
reasoning about how data change in the course of a process when reasoning
equationally about that process. We introduce an extension of ACP (Algebra of
Communicating Processes) with features that are relevant to processes in which
data are involved, present a Hoare logic for the processes considered in this
process algebra, and discuss the use of this Hoare logic as a complement to
pure equational reasoning with the equational axioms of the process algebra.

negative:
Continuous queries over data streams may suffer from blocking operations
and/or unbound wait, which may delay answers until some relevant input arrives
through the data stream. These delays may turn answers, when they arrive,
obsolete to users who sometimes have to make decisions with no help whatsoever.
Therefore, it can be useful to provide hypothetical answers - "given the
current information, it is possible that X will become true at time t" -
instead of no information at all.
  In this paper we present a semantics for queries and corresponding answers
that covers such hypothetical answers, together with an online algorithm for
updating the set of facts that are consistent with the currently available
information.

negative:
Bilateralists hold that the meanings of the connectives are determined by
rules of inference for their use in deductive reasoning with asserted and
denied formulas. This paper presents two bilateral connectives comparable to
Prior's tonk, for which, unlike for tonk, there are reduction steps for the
removal of maximal formulas arising from introducing and eliminating formulas
with those connectives as main operators. Adding either of them to bilateral
classical logic results in an incoherent system. One way around this problem is
to count formulas as maximal that are the conclusion of reductio and major
premise of an elimination rule and to require their removability from
deductions. The main part of the paper consists in a proof of a normalisation
theorem for bilateral logic. The closing sections address philosophical
concerns whether the proof provides a satisfactory solution to the problem at
hand and confronts bilateralists with the dilemma that a bilateral notion of
stability sits uneasily with the core bilateral thesis.
  The Note corrects an error in one of the reduction steps in the paper.

negative:
Learning representations of words in a continuous space is perhaps the most
fundamental task in NLP, however words interact in ways much richer than vector
dot product similarity can provide. Many relationships between words can be
expressed set-theoretically, for example, adjective-noun compounds (eg. "red
cars"$\subseteq$"cars") and homographs (eg. "tongue"$\cap$"body" should be
similar to "mouth", while "tongue"$\cap$"language" should be similar to
"dialect") have natural set-theoretic interpretations. Box embeddings are a
novel region-based representation which provide the capability to perform these
set-theoretic operations. In this work, we provide a fuzzy-set interpretation
of box embeddings, and learn box representations of words using a set-theoretic
training objective. We demonstrate improved performance on various word
similarity tasks, particularly on less common words, and perform a quantitative
and qualitative analysis exploring the additional unique expressivity provided
by Word2Box.

negative:
In this paper, we propose generative probabilistic models for label
aggregation. We use Gibbs sampling and a novel variational inference algorithm
to perform the posterior inference. Empirical results show that our methods
consistently outperform state-of-the-art methods.

negative:
Approximate probabilistic inference algorithms are central to many fields.
Examples include sequential Monte Carlo inference in robotics, variational
inference in machine learning, and Markov chain Monte Carlo inference in
statistics. A key problem faced by practitioners is measuring the accuracy of
an approximate inference algorithm on a specific data set. This paper
introduces the auxiliary inference divergence estimator (AIDE), an algorithm
for measuring the accuracy of approximate inference algorithms. AIDE is based
on the observation that inference algorithms can be treated as probabilistic
models and the random variables used within the inference algorithm can be
viewed as auxiliary variables. This view leads to a new estimator for the
symmetric KL divergence between the approximating distributions of two
inference algorithms. The paper illustrates application of AIDE to algorithms
for inference in regression, hidden Markov, and Dirichlet process mixture
models. The experiments show that AIDE captures the qualitative behavior of a
broad class of inference algorithms and can detect failure modes of inference
algorithms that are missed by standard heuristics.

negative:
This paper describes an application of reinforcement learning to the mention
detection task. We define a novel action-based formulation for the mention
detection task, in which a model can flexibly revise past labeling decisions by
grouping together tokens and assigning partial mention labels. We devise a
method to create mention-level episodes and we train a model by rewarding
correctly labeled complete mentions, irrespective of the inner structure
created. The model yields results which are on par with a competitive
supervised counterpart while being more flexible in terms of achieving targeted
behavior through reward modeling and generating internal mention structure,
especially on longer mentions.

negative:
It is suggested that an AI inference system should reflect an inference
policy that is tailored to the domain of problems to which it is applied -- and
furthermore that an inference policy need not conform to any general theory of
rational inference or induction. We note, for instance, that Bayesian reasoning
about the probabilistic characteristics of an inference domain may result in
the specification of an nonBayesian procedure for reasoning within the
inference domain. In this paper, the idea of an inference policy is explored in
some detail. To support this exploration, the characteristics of some standard
and nonstandard inference policies are examined.

negative:
Inter-domain Gaussian processes (GPs) allow for high flexibility and low
computational cost when performing approximate inference in GP models. They are
particularly suitable for modeling data exhibiting global structure but are
limited to stationary covariance functions and thus fail to model
non-stationary data effectively. We propose Inter-domain Deep Gaussian
Processes, an extension of inter-domain shallow GPs that combines the
advantages of inter-domain and deep Gaussian processes (DGPs), and demonstrate
how to leverage existing approximate inference methods to perform simple and
scalable approximate inference using inter-domain features in DGPs. We assess
the performance of our method on a range of regression tasks and demonstrate
that it outperforms inter-domain shallow GPs and conventional DGPs on
challenging large-scale real-world datasets exhibiting both global structure as
well as a high-degree of non-stationarity.

negative:
We propose a general framework for modelling and solving deductive games,
where one player selects a secret code and the other player strives to discover
this code using a minimal number of allowed experiments that reveal some
partial information about the code. The framework is implemented in a software
tool Cobra, and its functionality is demonstrated by producing new results
about existing deductive games.

negative:
Lifted probabilistic inference algorithms exploit regularities in the
structure of graphical models to perform inference more efficiently. More
specifically, they identify groups of interchangeable variables and perform
inference once per group, as opposed to once per variable. The groups are
defined by means of constraints, so the flexibility of the grouping is
determined by the expressivity of the constraint language. Existing approaches
for exact lifted inference use specific languages for (in)equality constraints,
which often have limited expressivity. In this article, we decouple lifted
inference from the constraint language. We define operators for lifted
inference in terms of relational algebra operators, so that they operate on the
semantic level (the constraints extension) rather than on the syntactic level,
making them language-independent. As a result, lifted inference can be
performed using more powerful constraint languages, which provide more
opportunities for lifting. We empirically demonstrate that this can improve
inference efficiency by orders of magnitude, allowing exact inference where
until now only approximate inference was feasible.

negative:
We study the model-checking problem for a quantitative extension of the modal
mu-calculus on a class of hybrid systems. Qualitative model checking has been
proved decidable and implemented for several classes of systems, but this is
not the case for quantitative questions that arise naturally in this context.
Recently, quantitative formalisms that subsume classical temporal logics and
allow the measurement of interesting quantitative phenomena were introduced. We
show how a powerful quantitative logic, the quantitative mu-calculus, can be
model checked with arbitrary precision on initialised linear hybrid systems. To
this end, we develop new techniques for the discretisation of continuous state
spaces based on a special class of strategies in model-checking games and
present a reduction to a class of counter parity games.

negative:
Substructural logics naturally support a quantitative interpretation of
formulas, as they are seen as consumable resources. Distances are the
quantitative counterpart of equivalence relations: they measure how much two
objects are similar, rather than just saying whether they are equivalent or
not. Hence, they provide the natural choice for modelling equality in a
substructural setting. In this paper, we develop this idea, using the
categorical language of Lawvere's doctrines. We work in a minimal fragment of
Linear Logic enriched by graded modalities, which are needed to write a
resource sensitive substitution rule for equality, enabling its quantitative
interpretation as a distance. We introduce both a deductive calculus and the
notion of Lipschitz doctrine to give it a sound and complete categorical
semantics. The study of 2-categorical properties of Lipschitz doctrines
provides us with a universal construction, which generates examples based for
instance on metric spaces and quantitative realisability. Finally, we show how
to smoothly extend our results to richer substructural logics, up to full
Linear Logic with quantifiers.

negative:
Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. This paper investigates how classical
inference and learning tasks known from the graphical model community can be
tackled for probabilistic logic programs. Several such tasks such as computing
the marginals given evidence and learning from (partial) interpretations have
not really been addressed for probabilistic logic programs before.
  The first contribution of this paper is a suite of efficient algorithms for
various inference tasks. It is based on a conversion of the program and the
queries and evidence to a weighted Boolean formula. This allows us to reduce
the inference tasks to well-studied tasks such as weighted model counting,
which can be solved using state-of-the-art methods known from the graphical
model and knowledge compilation literature. The second contribution is an
algorithm for parameter estimation in the learning from interpretations
setting. The algorithm employs Expectation Maximization, and is built on top of
the developed inference algorithms.
  The proposed approach is experimentally evaluated. The results show that the
inference algorithms improve upon the state-of-the-art in probabilistic logic
programming and that it is indeed possible to learn the parameters of a
probabilistic logic program from interpretations.

negative:
The language of epistemic specifications and epistemic logic programs extends
disjunctive logic programs under the stable model semantics with modal
constructs called subjective literals. Using subjective literals, it is
possible to check whether a regular literal is true in every or some stable
models of the program, those models, in this context also called \emph{belief
sets}, being collected in a set called world view. This allows for
representing, within the language, whether some proposition should be
understood accordingly to the open or the closed world assumption. Several
attempts for capturing the intuitions underlying the language by means of a
formal semantics were given, resulting in a multitude of proposals that makes
it difficult to understand the current state of the art. In this paper, we
provide an overview of the inception of the field and the knowledge
representation and reasoning tasks it is suitable for. We also provide a
detailed analysis of properties of proposed semantics, and an outlook of
challenges to be tackled by future research in the area. Under consideration in
Theory and Practice of Logic Programming (TPLP)

negative:
Model counting is the problem of computing the number of models that satisfy
a given propositional theory. It has recently been applied to solving inference
tasks in probabilistic logic programming, where the goal is to compute the
probability of given queries being true provided a set of mutually independent
random variables, a model (a logic program) and some evidence. The core of
solving this inference task involves translating the logic program to a
propositional theory and using a model counter. In this paper, we show that for
some problems that involve inductive definitions like reachability in a graph,
the translation of logic programs to SAT can be expensive for the purpose of
solving inference tasks. For such problems, direct implementation of stable
model semantics allows for more efficient solving. We present two
implementation techniques, based on unfounded set detection, that extend a
propositional model counter to a stable model counter. Our experiments show
that for particular problems, our approach can outperform a state-of-the-art
probabilistic logic programming solver by several orders of magnitude in terms
of running time and space requirements, and can solve instances of
significantly larger sizes on which the current solver runs out of time or
memory.

negative:
A modal logic that is strong enough to fully characterize the behavior of a
system is called expressive. Recently, with the growing diversity of systems to
be reasoned about (probabilistic, cyber-physical, etc.), the focus shifted to
quantitative settings which resulted in a number of expressivity results for
quantitative logics and behavioral metrics. Each of these quantitative
expressivity results uses a tailor-made argument; distilling the essence of
these arguments is non-trivial, yet important to support the design of
expressive modal logics for new quantitative settings. In this paper, we
present the first categorical framework for deriving quantitative expressivity
results, based on the new notion of approximating family. A key ingredient is
the codensity lifting -- a uniform observation-centric construction of various
bisimilarity-like notions such as bisimulation metrics. We show that several
recent quantitative expressivity results (e.g. by K\"onig et al. and by
Fijalkow et al.) are accommodated in our framework; a new expressivity result
is derived, too, for what we call bisimulation uniformity.

negative:
We present a formal logic for quantitative reasoning about security
properties of network protocols. The system allows us to derive concrete
security bounds that can be used to choose key lengths and other security
parameters. We provide axioms for reasoning about digital signatures and random
nonces, with security properties based on the concrete security of signature
schemes and pseudorandom number generators (PRG). The formal logic supports
first-order reasoning and reasoning about protocol invariants, taking concrete
security bounds into account. Proofs constructed in our logic also provide
conventional asymptotic security guarantees because of the way that concrete
bounds accumulate in proofs. As an illustrative example, we use the formal
logic to prove an authentication property with concrete bounds of a
signature-based challenge-response protocol.

negative:
This paper introduces a new technique for quantifying the approximation error
of a broad class of probabilistic inference programs, including ones based on
both variational and Monte Carlo approaches. The key idea is to derive a
subjective bound on the symmetrized KL divergence between the distribution
achieved by an approximate inference program and its true target distribution.
The bound's validity (and subjectivity) rests on the accuracy of two auxiliary
probabilistic programs: (i) a "reference" inference program that defines a gold
standard of accuracy and (ii) a "meta-inference" program that answers the
question "what internal random choices did the original approximate inference
program probably make given that it produced a particular result?" The paper
includes empirical results on inference problems drawn from linear regression,
Dirichlet process mixture modeling, HMMs, and Bayesian networks. The
experiments show that the technique is robust to the quality of the reference
inference program and that it can detect implementation bugs that are not
apparent from predictive performance.

negative:
In this paper we study the uses and the semantics of non-monotonic negation
in probabilistic deductive data bases. Based on the stable semantics for
classical logic programming, we introduce the notion of stable formula,
functions. We show that stable formula, functions are minimal fixpoints of
operators associated with probabilistic deductive databases with negation.
Furthermore, since a. probabilistic deductive database may not necessarily have
a stable formula function, we provide a stable class semantics for such
databases. Finally, we demonstrate that the proposed semantics can handle
default reasoning naturally in the context of probabilistic deduction.

negative:
This work presents how persistent predicates have been included in the
in-memory deductive system DES by relying on external SQL database management
systems. We introduce how persistence is supported from a user-point of view
and the possible applications the system opens up, as the deductive expressive
power is projected to relational databases. Also, we describe how it is
possible to intermix computations of the deductive engine and the external
database, explaining its implementation and some optimizations. Finally, a
performance analysis is undertaken, comparing the system with current
relational database systems.

negative:
Named entity linking is to map an ambiguous mention in documents to an entity
in a knowledge base. The named entity linking is challenging, given the fact
that there are multiple candidate entities for a mention in a document. It is
difficult to link a mention when it appears multiple times in a document, since
there are conflicts by the contexts around the appearances of the mention. In
addition, it is difficult since the given training dataset is small due to the
reason that it is done manually to link a mention to its mapping entity. In the
literature, there are many reported studies among which the recent embedding
methods learn vectors of entities from the training dataset at document level.
To address these issues, we focus on how to link entity for mentions at a
sentence level, which reduces the noises introduced by different appearances of
the same mention in a document at the expense of insufficient information to be
used. We propose a new unified embedding method by maximizing the relationships
learned from knowledge graphs. We confirm the effectiveness of our method in
our experimental studies.

negative:
Bayesian inference on structured models typically relies on the ability to
infer posterior distributions of underlying hidden variables. However,
inference in implicit models or complex posterior distributions is hard. A
popular tool for learning implicit models are generative adversarial networks
(GANs) which learn parameters of generators by fooling discriminators.
Typically, GANs are considered to be models themselves and are not understood
in the context of inference. Current techniques rely on inefficient global
discrimination of joint distributions to perform learning, or only consider
discriminating a single output variable. We overcome these limitations by
treating GANs as a basis for likelihood-free inference in generative models and
generalize them to Bayesian posterior inference over factor graphs. We propose
local learning rules based on message passing minimizing a global divergence
criterion involving cooperating local adversaries used to sidestep explicit
likelihood evaluations. This allows us to compose models and yields a unified
inference and learning framework for adversarial learning. Our framework treats
model specification and inference separately and facilitates richly structured
models within the family of Directed Acyclic Graphs, including components such
as intractable likelihoods, non-differentiable models, simulators and generally
cumbersome models. A key result of our treatment is the insight that Bayesian
inference on structured models can be performed only with sampling and
discrimination when using nonparametric variational families, without access to
explicit distributions. As a side-result, we discuss the link to likelihood
maximization. These approaches hold promise to be useful in the toolbox of
probabilistic modelers and enrich the gamut of current probabilistic
programming applications.