In the fast-growing area of Artificial Intelligence (AI), the ability of autonomous agents to engage in complex debates is crucial for consensus building on beliefs, actions, or goals and forms the basis for applications in decision-making, planning, opinion polling, and negotiation. In this paper, we leverage the Timed Concurrent Language for Argumentation, a modelling language derived from concurrent programming paradigms and Argumentation Theory, to introduce well-known high-level propositions (claim, counter, why, argue, concede, and retract) to model various debate forms, making it a powerful tool for agent interaction. The obtained constructs, specifically designed for multi-agent reasoning and the facilitation of argumentation, define the dialogue language DICLA (DIalogic Concurrent Language for Argumentation) that enables domain experts to employ advanced computational argumentation tools without needing programming skills, bridging the gap between theoretical argumentation models and practical, real-world applications.

Modelling Dialogues in a Concurrent Language for Argumentation

Bistarelli S.;Taticchi C.
2024

Abstract

In the fast-growing area of Artificial Intelligence (AI), the ability of autonomous agents to engage in complex debates is crucial for consensus building on beliefs, actions, or goals and forms the basis for applications in decision-making, planning, opinion polling, and negotiation. In this paper, we leverage the Timed Concurrent Language for Argumentation, a modelling language derived from concurrent programming paradigms and Argumentation Theory, to introduce well-known high-level propositions (claim, counter, why, argue, concede, and retract) to model various debate forms, making it a powerful tool for agent interaction. The obtained constructs, specifically designed for multi-agent reasoning and the facilitation of argumentation, define the dialogue language DICLA (DIalogic Concurrent Language for Argumentation) that enables domain experts to employ advanced computational argumentation tools without needing programming skills, bridging the gap between theoretical argumentation models and practical, real-world applications.
2024
9783031742088
9783031742095
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1586793
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