Computational complexity of flat and generic Assumption-Based Argumentation, with and without probabilities
作者:
Highlights:
• We study the computational complexity of Assumption-Based Argumentation (ABA) for classical argumentation decision problems.
• We establish the computational complexity of function problems in Probabilistic ABA (PABA) to be in the class FP#P.
• We provide a comprehensive picture of the computational complexity of ABA and PABA under several argumentation semantics.
摘要
Reasoning with probabilistic information has recently attracted considerable attention in argumentation, and formalisms of Probabilistic Abstract Argumentation (PAA), Probabilistic Bipolar Argumentation (PBA) and Probabilistic Structured Argumentation (PSA) have been proposed. These foundational advances have been complemented with investigations on the complexity of some approaches to PAA and PBA, but not to PSA. We study the complexity of an existing form of PSA, namely Probabilistic Assumption-Based Argumentation (PABA), a powerful, implemented formalism which subsumes several forms of PAA and other forms of PSA. Specifically, we establish membership (general upper bounds) and completeness (instantiated lower bounds) of reasoning in PABA for the class FP#P (of functions with a #P-oracle for counting the solutions of an NP problem) with respect to newly introduced probabilistic verification, credulous and sceptical acceptance function problems under several ABA semantics. As a by-product necessary to establish PABA complexity results, we provide a comprehensive picture of the ABA complexity landscape (for both flat and generic, possibly non-flat ABA) for the classical decision problems of verification, existence, credulous and sceptical acceptance under those ABA semantics.
论文关键词:Complexity,Probabilistic argumentation,Structured argumentation,Assumption-Based Argumentation
论文评审过程:Received 11 December 2019, Revised 18 September 2020, Accepted 29 December 2020, Available online 5 January 2021, Version of Record 20 January 2021.
论文官网地址:https://doi.org/10.1016/j.artint.2020.103449