Learning strategies for task delegation in norm-governed environments

Chukwuemeka David Emele, Timothy J Norman, Murat Sensoy, Simon Parsons

Research output: Contribution to journalArticle

4 Citations (Scopus)
1 Downloads (Pure)

Abstract

How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge 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 from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e.g., policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.
Original languageEnglish
Pages (from-to)499-525
Number of pages27
JournalAutonomous Agents and Multi-Agent Systems
Volume25
Issue number3
Early online date15 Mar 2012
DOIs
Publication statusPublished - Nov 2012

Keywords

  • norms
  • task delegation
  • ontology reasoning
  • decision theory
  • machine learning
  • argumentation
  • argumentation strategies

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