A Tableau Method for Verifying Dialogue Game Protocols for Agent Communication

Lecture Notes in Computer Science - Declarative Agent Languages and Technologies III ◽

10.1007/11691792_14 ◽

2006 ◽

pp. 223-244 ◽

Cited By ~ 2

Author(s):

Jamal Bentahar ◽

Bernard Moulin ◽

John-Jules Ch. Meyer

Keyword(s):

Agent Communication ◽

Dialogue Game ◽

Tableau Method

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Commitment-based and dialogue-game-based protocols: new trends in agent communication languages

The Knowledge Engineering Review ◽

10.1017/s0269888902000486 ◽

2002 ◽

Vol 17 (2) ◽

pp. 157-179 ◽

Cited By ~ 43

Author(s):

N. MAUDET ◽

B. CHAIB-DRAA

Keyword(s):

Point Of View ◽

Agent Communication ◽

Dialogue Game ◽

Agent Communication Languages ◽

General Constraints

This survey introduces existing approaches to Agent Communication Languages (ACLs) and particularly Conversation Policies (CPs) which can be viewed as general constraints on the sequence of semantically coherent messages leading to a goal. Then limitations of these CPs are discussed in detail, particularly limitations on flexibility and specification. Finally, ACLs are viewed from the dialectic point of view, and some approaches are introduced in this context: some focusing on commitment-based protocols and others on dialogue-game-based protocols.

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EVA 2.0: Emotional and rational multimodal argumentation between virtual agents

it - Information Technology ◽

10.1515/itit-2020-0050 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Niklas Rach ◽

Klaus Weber ◽

Yuchi Yang ◽

Stefan Ultes ◽

Elisabeth André ◽

...

Keyword(s):

Reinforcement Learning ◽

User Feedback ◽

Virtual Agents ◽

Multi Agent System ◽

Dialogue Game ◽

Multi Agent ◽

The Individual ◽

Minimal Bias ◽

Intuitive Interface ◽

Emotional Level

Abstract Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface.

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