Encoding Argumentation Frameworks to Propositional Logic Systems
- URL: http://arxiv.org/abs/2503.07351v1
- Date: Mon, 10 Mar 2025 14:06:58 GMT
- Title: Encoding Argumentation Frameworks to Propositional Logic Systems
- Authors: Shuai Tang, Jiachao Wu, Ning Zhou,
- Abstract summary: This paper generalizes the encoding method by encoding $AF$s as logical formulas in different propositional logic systems.<n>It studies the relationship between models of an AF by argumentation semantics, including Dung's classical semantics and Gabbay's equational semantics.
- Score: 5.714813286590744
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The theory of argumentation frameworks ($AF$s) has been a useful tool for artificial intelligence. The research of the connection between $AF$s and logic is an important branch. This paper generalizes the encoding method by encoding $AF$s as logical formulas in different propositional logic systems. It studies the relationship between models of an AF by argumentation semantics, including Dung's classical semantics and Gabbay's equational semantics, and models of the encoded formulas by semantics of propositional logic systems. Firstly, we supplement the proof of the regular encoding function in the case of encoding $AF$s to the 2-valued propositional logic system. Then we encode $AF$s to 3-valued propositional logic systems and fuzzy propositional logic systems and explore the model relationship. This paper enhances the connection between $AF$s and propositional logic systems. It also provides a new way to construct new equational semantics by choosing different fuzzy logic operations.
Related papers
- Logic-Based Artificial Intelligence Algorithms Supporting Categorical Semantics [0.0]
We develop forward chaining and normal form algorithms for reasoning about objects in cartesian categories with the rules for Horn logic.
We also adapt first-order unification to support multi-sorted theories, contexts, and fragments of first-order logic.
arXiv Detail & Related papers (2025-04-27T18:02:02Z) - A Semantic Parsing Algorithm to Solve Linear Ordering Problems [2.23890712706409]
We develop an algorithm to semantically parse linear ordering problems.
Our method takes as input a number of premises and candidate statements.
We then utilize constraint logic programming to infer the truth of proposed statements about the ordering.
arXiv Detail & Related papers (2025-02-12T13:58:42Z) - Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework [93.59256448185954]
We propose a logic-complete reasoning framework, Aristotle, with three key components: Logical Decomposer, Logical Search Router, and Logical Resolver.<n>In our framework, symbolic expressions and logical rules are comprehensively integrated into the entire reasoning process.<n>The experimental results on several datasets demonstrate that Aristotle consistently outperforms state-of-the-art reasoning frameworks in both accuracy and efficiency.
arXiv Detail & Related papers (2024-12-22T10:14:09Z) - Parameterized Dynamic Logic -- Towards A Cyclic Logical Framework for General Program Specification and Verification [0.174048653626208]
We present a theory of parameterized dynamic logic, namely DLp, for specifying and reasoning about a rich set of program models.<n>It provides a flexible verification framework based on the theories of dynamic logics.
arXiv Detail & Related papers (2024-04-28T07:08:44Z) - LINC: A Neurosymbolic Approach for Logical Reasoning by Combining
Language Models with First-Order Logic Provers [60.009969929857704]
Logical reasoning is an important task for artificial intelligence with potential impacts on science, mathematics, and society.
In this work, we reformulating such tasks as modular neurosymbolic programming, which we call LINC.
We observe significant performance gains on FOLIO and a balanced subset of ProofWriter for three different models in nearly all experimental conditions we evaluate.
arXiv Detail & Related papers (2023-10-23T17:58:40Z) - 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) - 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) - A Logic-Based Framework for Natural Language Inference in Dutch [1.0178220223515955]
We present a framework for deriving relations between Dutch sentence pairs.
The proposed framework relies on logic-based reasoning to produce inspectable proofs leading up to inference labels.
We evaluate the reasoning pipeline on the recently created Dutch natural language inference dataset.
arXiv Detail & Related papers (2021-10-07T10:34:46Z) - Refining Labelled Systems for Modal and Constructive Logics with
Applications [0.0]
This thesis serves as a means of transforming the semantics of a modal and/or constructive logic into an 'economical' proof system.
The refinement method connects two proof-theoretic paradigms: labelled and nested sequent calculi.
The introduced refined labelled calculi will be used to provide the first proof-search algorithms for deontic STIT logics.
arXiv Detail & Related papers (2021-07-30T08:27:15Z) - 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) - Lexicographic Logic: a Many-valued Logic for Preference Representation [1.5484595752241122]
We propose lexicographic logic, an extension of classical propositional logic that can express a variety of preferences.
We argue that the new logic is an effective formalism for ranking query results according to the satisfaction level of user preferences.
arXiv Detail & Related papers (2020-12-20T14:42:04Z)
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.