Agent communication pragmatics: The cognitive coherence approach

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Abstract

Different approaches have investigated the syntax and semantics of agent communication languages. However, these approaches have not indicated how agents should dynamically use communications. Instead of filling this pragmatics gap, most approaches have mainly focused on the 'structure' of dialogues even though developers are more interested in agents' capabilities of having 'useful' automated conversations with respect to their goals rather than in their abilities to structure dialogues. This led us to work on a theory of the use of conversations between agents. In this paper, we propose a pragmatics theory which extends and adapts the cognitive dissonance theory (a major theory of social psychology) to multi-agent systems by unifying it with the theory of coherence in thought and action that issues from computational philosophy of mind. Precisely, we show how this theory allows us to provide generic conceptual tools for the automation of both agent communicational behavior and attitude change processes. This new motivational model is formulated in terms of constraints and elements of cognition and allows us to define cognitive incoherences and dialogue utility measures. We show how these measures could be used to solve common problems and answer some critical questions concerning agent communication frameworks use. Finally, our exploration in applying the cognitive coherence pragmatics theory as a new communication layer over classical BDI agents is presented. It relies on our dialogue games based agent communication language (DIAGAL) and our dialogue games simulator toolbox (DGS). The resulting framework provides the necessary theoretical and practical elements for implementing our theory. In doing so, it brings in a general scheme for automatizing agents' communicational behavior as it is exemplified in this article. © 2005 Elsevier B.V. All rights reserved.

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Pasquier, P., & Chaib-draa, B. (2005). Agent communication pragmatics: The cognitive coherence approach. Cognitive Systems Research, 6(4), 364–395. https://doi.org/10.1016/j.cogsys.2005.03.002

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