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

Vallati, Mauro, Cerutti, Federico and Giacomin, Massimiliano 2019. Predictive models and abstract argumentation: the case of high-complexity semantics. Knowledge Engineering Review 34 , e6. 10.1017/S0269888918000036

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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— i.e., values that summarise 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.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Cambridge University Press (CUP)
ISSN: 0269-8889
Date of First Compliant Deposit: 7 November 2017
Date of Acceptance: 3 November 2017
Last Modified: 16 Mar 2020 11:46

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