Argumentation strategies for collaborative plan resourcing

Chukwuemeka David Emele*, Timothy J. Norman, Simon Parsons

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


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.

Original languageEnglish
Title of host publicationArgumentation in Multi-Agent Systems - 8th International Workshop, ArgMAS 2011, Revised Selected Papers
Number of pages20
Publication statusPublished - 2012
Event8th International Workshop on Argumentation in Multi-Agent Systems, ArgMAS 2011 - Taipei, Taiwan, Province of China
Duration: 3 May 20113 May 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7543 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Workshop on Argumentation in Multi-Agent Systems, ArgMAS 2011
Country/TerritoryTaiwan, Province of China


  • Argumentation strategies
  • Decision theory
  • Dialogue
  • Machine learning
  • Norms
  • Plan resourcing
  • Policies


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