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Forgetting an Argument
Baumann, Ringo; Gabbay, Dov M.; Rodrigues, Odinaldo
2020In Proceedings of the AAAI Conference on Artificial Intelligence
Peer reviewed
 

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Abstract :
[en] The notion of forgetting, as considered in the famous paper by Lin and Reiter in 1994 has been extensively studied in classical logic and more recently, in non-monotonic formalisms like logic programming. In this paper, we convey the idea of forgetting to another major AI formalism, namely Dung-style argumentation frameworks. Our approach is axiomatic-driven and not limited to any specific semantics: we propose semantical and syntactical desiderata encoding different criteria for what forgetting an argument might mean; analyze how these criteria relate to each other; and check whether the criteria can be satisfied in general. The analysis is done for a number of widely used argumentation semantics. Our investigation shows that almost all desiderata are individually satisfiable. However, combinations of semantical and/or syntactical conditions reveal a much more interesting landscape. For instance, we found that the ad hoc approach to forgetting an argument, i.e., by the syntactical removal of the argument and all of its associated attacks, is too restrictive and only compatible with the two weakest semantical desiderata. Amongst the several interesting combinations identified, we showed that one satisfies a notion of minimal change and presented an algorithm that given an AF F and argument x, constructs a suitable AF G satisfying the conditions in the combination.
Disciplines :
Computer science
Author, co-author :
Baumann, Ringo
Gabbay, Dov M. ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Rodrigues, Odinaldo
External co-authors :
yes
Language :
English
Title :
Forgetting an Argument
Publication date :
2020
Event name :
The thirty-fourth AAAI conference on Artificial Intelligence (AAAI-20)
Event date :
February 7-12 2020
Main work title :
Proceedings of the AAAI Conference on Artificial Intelligence
Pages :
2750--2757
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 29 March 2021

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