Abstract
In formal argumentation, a distinction can be made between extension-based semantics, where sets of arguments are either (jointly) accepted or not, and ranking-based semantics, where grades of acceptability are assigned to arguments. Another important distinction is that between abstract approaches, that abstract away from the content of arguments, and structured approaches, that specify a method of constructing argument graphs on the basis of a knowledge base. While ranking-based semantics have been extensively applied to abstract argumentation, few work has been done on ranking-based semantics for structured argumentation. In this paper, we make a systematic investigation into the behaviour of ranking-based semantics applied to existing formalisms for structured argumentation. We show that a wide class of ranking-based semantics gives rise to so-called culpability measures, and are relatively robust to specific choices in argument construction methods.
Original language | English |
---|---|
Title of host publication | Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 |
Editors | Edith Elkind |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 3268-3276 |
Number of pages | 9 |
ISBN (Electronic) | 9781956792034 |
DOIs | |
Publication status | Published - 2023 |
Event | 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China Duration: 19 Aug 2023 → 25 Aug 2023 https://ijcai-23.org/ |
Publication series
Series | IJCAI International Joint Conference on Artificial Intelligence |
---|---|
Volume | 2023-August |
ISSN | 1045-0823 |
Conference
Conference | 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 |
---|---|
Abbreviated title | IJCAI 2023 |
Country/Territory | China |
City | Macao |
Period | 19/08/23 → 25/08/23 |
Internet address |