Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation

Dragan Doder, Leila Amgoud, Srdjan Vesic

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Compensation is a strategy that a semantics may
follow when it faces dilemmas between quality and
quantity of attackers. It allows several weak attacks to compensate one strong attack. It is based
on compensation degree, which is a tuple that indicates (i) to what extent an attack is weak and
(ii) the number of weak attacks needed to compensate a strong one. Existing principles on compensation do not specify the parameters, thus it is
unclear whether semantics satisfying them compensate at only one degree or several degrees, and
which ones. This paper proposes a parameterised
family of gradual semantics, which unifes multiple
semantics that share some principles but differ in
their strategy regarding solving dilemmas. Indeed,
we show that the two semantics taking the extreme
values of the parameter favour respectively quantity and quality, while all the remaining ones compensate at some degree. We defne three classes of
compensation degrees and show that the novel family is able to compensate at all of them while none
of the existing gradual semantics does.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023
Publication statusPublished - 2023


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