Automata for infinite argumentation structures

Baroni, Pietro, Cerutti, Federico ORCID: https://orcid.org/0000-0003-0755-0358, Dunne, Paul and Giacomin, Massimiliano 2013. Automata for infinite argumentation structures. Artificial Intelligence 203 , pp. 104-150. 10.1016/j.artint.2013.05.002

Preview

PDF - Accepted Post-Print Version Download (621kB) | Preview

Abstract

The theory of abstract argumentation frameworks (afs) has, in the main, focused on finite structures, though there are many significant contexts where argumentation can be regarded as a process involving infinite objects. To address this limitation, in this paper we propose a novel approach for describing infinite afs using tools from formal language theory. In particular, the possibly infinite set of arguments is specified through the language recognized by a deterministic finite automaton while a suitable formalism, called attack expression, is introduced to describe the relation of attack between arguments. The proposed approach is shown to satisfy some desirable properties which cannot be achieved through other “naive” uses of formal languages. In particular, the approach is shown to be expressive enough to capture (besides any arbitrary finite structure) a large variety of infinite afs including two major examples from previous literature and two sample cases from the domains of multi-agent negotiation and ambient intelligence. On the computational side, we show that several decision and construction problems which are known to be polynomial time solvable in finite afs are decidable in the context of the proposed formalism and we provide the relevant algorithms. Moreover we obtain additional results concerning the case of finitaryafs.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Elsevier
ISSN:	0004-3702
Date of First Compliant Deposit:	16 October 2018
Date of Acceptance:	2013
Last Modified:	07 Nov 2023 03:20
URI:	https://orca.cardiff.ac.uk/id/eprint/115754

Citation Data

Cited 26 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item