Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation
Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation
Dragan Doder, Leila Amgoud, Srdjan Vesic
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 3176-3183.
https://doi.org/10.24963/ijcai.2023/354
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
unifies 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 define 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.
Keywords:
Knowledge Representation and Reasoning: KRR: Argumentation