A Unifying Framework for Learning Argumentation Semantics
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929739778752512 |
|---|---|
| author | Mileva, Zlatina Bikakis, Antonis D'Asaro, Fabio Aurelio Law, Mark Russo, Alessandra |
| author_facet | Mileva, Zlatina Bikakis, Antonis D'Asaro, Fabio Aurelio Law, Mark Russo, Alessandra |
| contents | Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_12309 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Unifying Framework for Learning Argumentation Semantics Mileva, Zlatina Bikakis, Antonis D'Asaro, Fabio Aurelio Law, Mark Russo, Alessandra Artificial Intelligence Machine Learning Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues. |
| title | A Unifying Framework for Learning Argumentation Semantics |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2310.12309 |