Argumentation strategies for task delegation

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

*Corresponding author for this work

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


What argument(s) do I put forward in order to persuade another agent to do something for me? This is an important question for an autonomous agent collaborating with others to solve a problem. How effective were similar arguments in convincing similar agents in similar circumstances? What are the risks associated with putting certain arguments forward? Can agents exploit evidence derived from past dialogues to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence derived from dialogues, and where these models are used to guide future argumentation strategy. We combine argumentation, machine learning and decision theory in a novel way that enables agents to reason about constraints (e.g., policies) that others are operating within, and make informed decisions about whom to delegate a task to. We demonstrate the utility of this novel approach through empirical evaluation in a plan resourcing domain. Our evaluation shows that a combination of decision-theoretic and machine learning techniques can significantly help to improve dialogical outcomes.

Original languageEnglish
Title of host publicationMulti-Agent Systems
Subtitle of host publication9th European Workshop, EUMAS 2011, Revised Selected Papers
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783642347986
Publication statusPublished - 2012
Event9th European Workshop on Multi-Agent Systems, EUMAS 2011 - Maastricht, Netherlands
Duration: 14 Nov 201115 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7541 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th European Workshop on Multi-Agent Systems, EUMAS 2011


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