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Predictive models and abstract argumentation: the case of high-complexity semantics

Published online by Cambridge University Press:  18 April 2019

Mauro Vallati ,
Federico Cerutti Open the ORCID record for Federico Cerutti [Opens in a new window]  and
Massimiliano Giacomin
Mauro Vallati
Affiliation:
School of Computing & Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK e-mail: m.vallati@hud.ac.uk
Federico Cerutti
Affiliation:
School of Computer Science & Informatics, Cardiff University, Cardiff CF24 3AA, UK e-mail: CeruttiF@cardiff.ac.uk
Massimiliano Giacomin
Affiliation:
Dipartimento di Ingegneria dell’Infomazione, Università degli Studi di Brescia, via Branze 38, Brescia, Italy e-mail: massimiliano.giacomin@unibs.it
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Abstract

In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features—that is, values that summarize a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way.

Information

Type
Principles and Practice of Multi-Agent Systems
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e6
Copyright
© Cambridge University Press, 2019 

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