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Learning strategies for task delegation in norm-governed environments

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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.

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Authors and Affiliations

  1. Department of Computing Science, University of Aberdeen, AB24 3UE, Aberdeen, UK

    Chukwuemeka David Emele, Timothy J. Norman & Murat Şensoy

  2. Department of Computing Science, Brooklyn College, City University of New York, New York, NY, 11210, USA

    Simon Parsons

Authors
  1. Chukwuemeka David Emele
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  2. Timothy J. Norman
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  3. Murat Şensoy
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  4. Simon Parsons
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Correspondence to Chukwuemeka David Emele.

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Emele, C.D., Norman, T.J., Şensoy, M. et al. Learning strategies for task delegation in norm-governed environments. Auton Agent Multi-Agent Syst 25, 499–525 (2012). https://doi.org/10.1007/s10458-012-9194-9

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