On syntactically similar logic programs and sequential decompositions
- URL: http://arxiv.org/abs/2109.05300v3
- Date: Mon, 11 Dec 2023 22:32:48 GMT
- Title: On syntactically similar logic programs and sequential decompositions
- Authors: Christian Antic
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. The author has recently introduced the
sequential composition of logic programs in the context of logic-based
analogical reasoning and learning in logic programming. Motivated by these
applications, in this paper we construct a qualitative and algebraic notion of
syntactic logic program similarity from sequential decompositions of programs.
We then show how similarity can be used to answer queries across different
domains via a one-step reduction. In a broader sense, this paper is a further
step towards an algebraic theory of logic programming.
Related papers
- 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) - When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities.
Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity.
Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
arXiv Detail & Related papers (2023-08-29T17:22:39Z) - 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) - Argumentative Characterizations of (Extended) Disjunctive Logic Programs [2.055949720959582]
We show that assumption-based argumentation can represent not only normal logic programs, but also disjunctive logic programs and their extensions.
We consider some inference rules for disjunction that the core logic of the argumentation frameworks should respect.
arXiv Detail & Related papers (2023-06-12T14:01:38Z) - The Transformation Logics [58.35574640378678]
We introduce a new family of temporal logics designed to balance the trade-off between expressivity and complexity.
Key feature is the possibility of defining operators of a new kind that we call transformation operators.
We show them to yield logics capable of creating hierarchies of increasing expressivity and complexity.
arXiv Detail & Related papers (2023-04-19T13:24:04Z) - Sequential decomposition of propositional logic programs [0.0]
This paper studies the sequential decomposition of programs by studying Green's relations between programs.
In a broader sense, this paper is a further step towards an algebraic theory of logic programming.
arXiv Detail & Related papers (2023-02-21T16:14:57Z) - 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) - GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic
Synthesis [34.54658276390227]
Deep reinforcement learning (DRL) lacks high-order intelligence regarding learning and generalization in complex problems.
Previous works attempt to directly synthesize a white-box logic program as the DRL policy, manifesting logic-driven behaviors.
We propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
arXiv Detail & Related papers (2022-05-27T02:50:13Z) - Logic-Driven Context Extension and Data Augmentation for Logical
Reasoning of Text [65.24325614642223]
We propose to understand logical symbols and expressions in the text to arrive at the answer.
Based on such logical information, we put forward a context extension framework and a data augmentation algorithm.
Our method achieves the state-of-the-art performance, and both logic-driven context extension framework and data augmentation algorithm can help improve the accuracy.
arXiv Detail & Related papers (2021-05-08T10:09:36Z) - Higher-order Logic as Lingua Franca -- Integrating Argumentative
Discourse and Deep Logical Analysis [0.0]
We present an approach towards the deep, pluralistic logical analysis of argumentative discourse.
We use state-of-the-art automated reasoning technology for classical higher-order logic.
arXiv Detail & Related papers (2020-07-02T11:07:53Z)
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.