Abstract
Explainable artificial intelligence (XAI) has gained increasing interest in recent years in the argumentation community. In this paper we consider this topic in the context of logic-based argumentation, showing that the latter is a particularly promising paradigm for facilitating explainable AI. In particular, we provide two representations of abductive reasoning by sequent-based argumentation frameworks and show that such frameworks successfully cope with related challenges, such as the handling of synonyms, justifications, and logical equivalences.
| Original language | English |
|---|---|
| Title of host publication | Computational Models of Argument |
| Editors | Francesca Toni, Sylwia Polberg, Richard Booth, Martin Caminada, Hiroyuki Kido |
| Publisher | IOS Press |
| Pages | 32-43 |
| Number of pages | 12 |
| ISBN (Electronic) | 978-1-64368-307-2 |
| ISBN (Print) | 978-1-64368-306-5 |
| DOIs | |
| Publication status | Published - 7 Sept 2022 |
Publication series
| Name | Frontiers in Artificial Intelligence and Applications |
|---|---|
| Publisher | IOS Press |
| Volume | 353 |
| ISSN (Print) | 0922-6389 |
| ISSN (Electronic) | 1879-8314 |
Bibliographical note
Funding Information:1Supported by the Israel Science Foundation (grant 550/19).
Publisher Copyright:
© 2022 The authors and IOS Press. All rights reserved.
Keywords
- Explainable AI
- sequent-based argumentation
- abductive logics