{"type":"ScholarlyArticle","title":"Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation","authors":[{"name":"Dragan Doder","slug":"dragan-doder","given":"Dragan","family":"Doder","affiliation":"Utrecht University"},{"name":"Leila Amgoud","slug":"leila-amgoud","given":"Leila","family":"Amgoud","affiliation":"CNRS -IRIT"},{"name":"Srdjan Vesic","slug":"srdjan-vesic","given":"Srdjan","family":"Vesic","affiliation":"CRIL -CNRS -Univ. Artois"}],"abstract":"Compensation is a strategy that a semantics may follow when it faces  \r\ndilemmas between quality and quantity of attackers. It allows several weak \r\nattacks to compensate one strong attack. It is  based on compensation degree, \r\nwhich is a tuple that indicates (i) to what extent an attack is weak and (ii) the \r\nnumber of weak attacks needed to compensate a strong one. \r\nExisting principles on compensation do not specify the parameters, thus it is unclear \r\nwhether semantics satisfying them compensate at only one degree or several degrees, and which ones.\r\nThis paper proposes a parameterised family of gradual semantics, which \r\nunifies multiple semantics that share some principles but differ in their \r\nstrategy regarding solving dilemmas. Indeed, we show that the two semantics taking \r\nthe extreme values of the parameter favour respectively  quantity and quality,  while all \r\nthe remaining ones compensate at some degree. We define three classes of compensation \r\ndegrees and show that the novel family is able to compensate at all of them while \r\nnone of the existing gradual semantics does.","doi":"10.24963/ijcai.2023/354","url":"https://ijcai.org/proceedings/2023/354","pdf":"https://ijcai.org/proceedings/2023/0354.pdf","markdown":"https://ijcai.org/proceedings/2023/354.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/354","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":354,"pages":{"first":3176,"last":3183},"keywords":["Knowledge Representation and Reasoning: KRR: Argumentation"],"videos":[],"references":[{"position":1,"raw":"[Amgoud and Ben-Naim, 2013] Leila Amgoud and Jonathan Ben-Naim. 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