Three Modern Roles for Logic in AI

• URL: http://arxiv.org/abs/2004.08599v1
• Date: Sat, 18 Apr 2020 11:51:13 GMT
• Title: Three Modern Roles for Logic in AI
• Authors: Adnan Darwiche
• Abstract summary: We consider three modern roles for logic in artificial intelligence. These include computation, learning from a combination of data and knowledge, and reasoning about the behavior of machine learning systems.
• Score: 11.358487655918676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Empower Nested Boolean Logic via Self-Supervised Curriculum Learning [67.46052028752327]
  We find that any pre-trained language models even including large language models only behave like a random selector in the face of multi-nested logic. To empower language models with this fundamental capability, this paper proposes a new self-supervised learning method textitCurriculum Logical Reasoning (textscClr)
  arXiv  Detail & Related papers  (2023-10-09T06:54:02Z)
• LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning [73.98142349171552]
  LOGICSEG is a holistic visual semantic that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. During fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models.
  arXiv  Detail & Related papers  (2023-09-24T05:43:19Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Discourse-Aware Graph Networks for Textual Logical Reasoning [142.0097357999134]
  Passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence) We propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs) The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features.
  arXiv  Detail & Related papers  (2022-07-04T14:38:49Z)
• Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [65.23508422635862]
  We propose learning rules with the recently proposed logical neural networks (LNN) Compared to others, LNNs offer strong connection to classical Boolean logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable.
  arXiv  Detail & Related papers  (2021-12-06T19:38:30Z)
• On syntactically similar logic programs and sequential decompositions [0.0]
  Rule-based reasoning is an essential part of human intelligence prominently formalized in artificial intelligence research via logic programs. Describing complex objects as the composition of elementary ones is a common strategy in computer science and science in general. We show how similarity can be used to answer queries across different domains via a one-step reduction.
  arXiv  Detail & Related papers  (2021-09-11T15:22:17Z)
• Inductive logic programming at 30 [22.482292439881192]
  Inductive logic programming (ILP) is a form of logic-based machine learning. We focus on (i) new meta-level search methods, (ii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.
  arXiv  Detail & Related papers  (2021-02-21T08:37:17Z)
• Logic Tensor Networks [9.004005678155023]
  We present Logic Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning. We show that LTN provides a uniform language for the specification and the computation of several AI tasks.
  arXiv  Detail & Related papers  (2020-12-25T22:30:18Z)
• Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains [12.47276164048813]
  Tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. We report on results that challenge the view on the limitations of logic, and expose the role that logic can play for learning in infinite domains.
  arXiv  Detail & Related papers  (2020-06-15T15:29:49Z)
• Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
  We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
  arXiv  Detail & Related papers  (2020-03-09T15:04:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.