908 research outputs found
Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives
Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that
combining deep learning with symbolic reasoning will lead to stronger AI than
either paradigm on its own. As successful as deep learning has been, it is
generally accepted that even our best deep learning systems are not very good
at abstract reasoning. And since reasoning is inextricably linked to language,
it makes intuitive sense that Natural Language Processing (NLP), would be a
particularly well-suited candidate for NeSy. We conduct a structured review of
studies implementing NeSy for NLP, with the aim of answering the question of
whether NeSy is indeed meeting its promises: reasoning, out-of-distribution
generalization, interpretability, learning and reasoning from small data, and
transferability to new domains. We examine the impact of knowledge
representation, such as rules and semantic networks, language structure and
relational structure, and whether implicit or explicit reasoning contributes to
higher promise scores. We find that systems where logic is compiled into the
neural network lead to the most NeSy goals being satisfied, while other factors
such as knowledge representation, or type of neural architecture do not exhibit
a clear correlation with goals being met. We find many discrepancies in how
reasoning is defined, specifically in relation to human level reasoning, which
impact decisions about model architectures and drive conclusions which are not
always consistent across studies. Hence we advocate for a more methodical
approach to the application of theories of human reasoning as well as the
development of appropriate benchmarks, which we hope can lead to a better
understanding of progress in the field. We make our data and code available on
github for further analysis.Comment: Surve
OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond
An introduction to the OpenCog Hyperon framework for Artificiai General
Intelligence is presented. Hyperon is a new, mostly from-the-ground-up
rewrite/redesign of the OpenCog AGI framework, based on similar conceptual and
cognitive principles to the previous OpenCog version, but incorporating a
variety of new ideas at the mathematical, software architecture and
AI-algorithm level. This review lightly summarizes: 1) some of the history
behind OpenCog and Hyperon, 2) the core structures and processes underlying
Hyperon as a software system, 3) the integration of this software system with
the SingularityNET ecosystem's decentralized infrastructure, 4) the cognitive
model(s) being experimentally pursued within Hyperon on the hopeful path to
advanced AGI, 5) the prospects seen for advanced aspects like reflective
self-modification and self-improvement of the codebase, 6) the tentative
development roadmap and various challenges expected to be faced, 7) the
thinking of the Hyperon team regarding how to guide this sort of work in a
beneficial direction ... and gives links and references for readers who wish to
delve further into any of these aspects
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding
Logical reasoning remains a pivotal component within the realm of artificial
intelligence. The recent evolution of large language models (LLMs) has marked
significant progress in this domain. The adoption of strategies like
chain-of-thought (CoT) has enhanced the performance of LLMs across diverse
reasoning tasks. Nonetheless, logical reasoning that involves proof planning,
specifically those that necessitate the validation of explanation accuracy,
continues to present stumbling blocks. In this study, we first evaluate the
efficacy of LLMs with advanced CoT strategies concerning such tasks. Our
analysis reveals that LLMs still struggle to navigate complex reasoning chains,
which demand the meticulous linkage of premises to derive a cogent conclusion.
To address this issue, we finetune a smaller-scale language model, equipping it
to decompose proof objectives into more manageable subgoals. We also introduce
contrastive decoding to stepwise proof generation, making use of negative
reasoning paths to strengthen the model's capacity for logical deduction.
Experiments on EntailmentBank underscore the success of our method in
augmenting the proof planning abilities of language models
Topological Foundations of Cognitive Science
A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers:
** Topological Foundations of Cognitive Science, Barry Smith
** The Bounds of Axiomatisation, Graham White
** Rethinking Boundaries, Wojciech Zelaniec
** Sheaf Mereology and Space Cognition, Jean Petitot
** A Mereotopological Definition of 'Point', Carola Eschenbach
** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel
** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda
** Defining a 'Doughnut' Made Difficult, N .M. Gotts
** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts
** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi
** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Ki
Complex Knowledge Base Question Answering: A Survey
Knowledge base question answering (KBQA) aims to answer a question over a
knowledge base (KB). Early studies mainly focused on answering simple questions
over KBs and achieved great success. However, their performance on complex
questions is still far from satisfactory. Therefore, in recent years,
researchers propose a large number of novel methods, which looked into the
challenges of answering complex questions. In this survey, we review recent
advances on KBQA with the focus on solving complex questions, which usually
contain multiple subjects, express compound relations, or involve numerical
operations. In detail, we begin with introducing the complex KBQA task and
relevant background. Then, we describe benchmark datasets for complex KBQA task
and introduce the construction process of these datasets. Next, we present two
mainstream categories of methods for complex KBQA, namely semantic
parsing-based (SP-based) methods and information retrieval-based (IR-based)
methods. Specifically, we illustrate their procedures with flow designs and
discuss their major differences and similarities. After that, we summarize the
challenges that these two categories of methods encounter when answering
complex questions, and explicate advanced solutions and techniques used in
existing work. Finally, we conclude and discuss several promising directions
related to complex KBQA for future research.Comment: 20 pages, 4 tables, 7 figures. arXiv admin note: text overlap with
arXiv:2105.1164
Argumentation and Defeasible Reasoning in the Law
Different formalisms for defeasible reasoning have been used to represent knowledge and reason in the legal field. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: defeasible logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches under three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software resources). On top of that, two real examples in the legal domain are designed and implemented in ASPIC+ to showcase the benefit of an argumentation approach in real-world domains. The CrossJustice and Interlex projects are taken as a testbed, and experiments are conducted with the Arg2P technology
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