Buraglio, G., Dvořák, W., Rapberger, A., & Woltran, S. (2023). Constrained Derivation in Assumption-Based Argumentation. In G. Alfano & S. Ferilli (Eds.), Proceedings of the 7th Workshop on Advances in Argumentation in Artificial Intelligence (AI^3 2023). CEUR-WS.org. https://doi.org/10.34726/5385
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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Erschienen in:
Proceedings of the 7th Workshop on Advances in Argumentation in Artificial Intelligence (AI^3 2023)
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Band:
3546
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Datum (veröffentlicht):
12-Nov-2023
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Veranstaltungsname:
7th Workshop on Advances in Argumentation in Artificial (AI^3 2023) co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023)
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Veranstaltungszeitraum:
9-Nov-2023
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Veranstaltungsort:
Rom, Italien
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Umfang:
15
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Verlag:
CEUR-WS.org
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Peer Reviewed:
Ja
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Keywords:
Assumption-Based Argumentation; Normative Reasoning; Non-monotonic Reasoning; integral step; Control; Constrained
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Abstract:
Structured argumentation formalisms provide a rich framework to formalise and reason over situations where contradicting information is present. However, in most formalisms the integral step of constructing all possible arguments is performed in an unconstrained way and is thus not under direct control of the user. This can hinder a solid analysis of the behaviour of the system and makes explanations for the results difficult to obtain. In this work, we introduce a general approach that allows constraining the derivation of arguments for assumption-based argumentation.
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Projekttitel:
Revealing and Utilizing the Hidden Structure for Solving Hard Problems in AI: ICT19-065 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds) Logics for Computer Science Program at TU Wien: 101034440 (European Commission)
CC BY 4.0
