Abstract
An important question for an autonomous agent deciding whom to approach for a resource or for an action to be done is what do I need to say to convince you to do something? Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? In this paper we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues, and where these models are used to guide future argumentation strategy. We empirically evaluate our approach to demonstrate that decision-theoretic and machine learning techniques can both significantly improve the cumulative utility of dialogical outcomes, and help to reduce communication overhead.
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Emele, C.D., Norman, T.J., Parsons, S. (2012). Argumentation Strategies for Collaborative Plan Resourcing. In: McBurney, P., Parsons, S., Rahwan, I. (eds) Argumentation in Multi-Agent Systems. ArgMAS 2011. Lecture Notes in Computer Science(), vol 7543. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33152-7_10
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DOI: https://doi.org/10.1007/978-3-642-33152-7_10
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