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AI argumentation frameworks: Complexity mapped for grounded and preferred semantics

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This paper delves into the computational complexity of grounded and preferred semantics within finitary argumentation frameworks. Researchers have mapped the decision problems for these semantics, finding that while finitarity can reduce complexity to the arithmetical hierarchy, certain problems like skeptical acceptance and universal quantification still reside in the higher analytical hierarchy. The findings highlight the precise limits of finitarity in simplifying reasoning within these AI frameworks. AI

IMPACT Clarifies the computational limits of specific AI reasoning methods, informing future research in formal AI.

RANK_REASON Academic paper detailing computational complexity of AI reasoning frameworks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI argumentation frameworks: Complexity mapped for grounded and preferred semantics

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Academic paper detailing computational complexity of AI reasoning frameworks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jinfan Xu, Jieting Luo ·

    Complexity of Grounded Semantics and Preferred Semantics in Finitary Argumentation Frameworks

    arXiv:2610.12008v1 Announce Type: new Abstract: Abstract argumentation frameworks (AFs) introduced by Dung provide a formal foundation for non-monotonic reasoning in artificial intelligence. While decision problems for general infinite AFs typically reside at high levels of the a…

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