New stochastic local search approaches for computing preferred extensions of abstract argumentation

DOI10.3233/AIC-180769zbMath1462.68184OpenAlexW2804949991WikidataQ129820467 ScholiaQ129820467MaRDI QIDQ5145445

Dangdang Niu, Lei Liu, Shuai Lu

Publication date: 20 January 2021

Published in: AI Communications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3233/aic-180769

zbMATH Keywords

stochastic local searchabstract argumentationpreferred semantics

Mathematics Subject Classification ID

Logic in artificial intelligence (68T27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (1)

Using Graph Convolutional Networks for Approximate Reasoning with Abstract Argumentation Frameworks: A Feasibility Study

Uses Software

• CCLS

Cites Work

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• Computing Preferred Extensions in Abstract Argumentation: A SAT-Based Approach
• Improved Separations of Regular Resolution from Clause Learning Proof Systems
• Analyzing Kleinberg's (and other) small-world Models
• Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolution

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