A Framework for Goal-Based Semantic Compensation in Agent Systems

  • Amy Unruh
  • James Bailey
  • Kotagiri Ramamohanarao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4324)


This paper describes an approach to improving the robustness of an agent system by augmenting its failure-handling capabilities. The approach is based on the concept of semantic compensation: “cleaning up” failed or canceled tasks can help agents behave more robustly and predictably at both an individual and system level. Our approach is goal-based, both with respect to defining failure-handling knowledge, and in specifying a failure-handling model that makes use of this knowledge. By abstracting failure-handling above the level of specific actions or task implementations, it is not tied to specific agent architectures or task plans and is more widely applicable. The failure-handling knowledge is employed via a failure-handling support component associated with each agent through a goal-based interface. The use of this component decouples the specification and use of failure-handling information from the specification of the agent’s domain problem-solving knowledge, and reduces the failure-handling information that an agent developer needs to provide.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Amy Unruh
    • 1
  • James Bailey
    • 1
  • Kotagiri Ramamohanarao
    • 1
  1. 1.Dept. of Computer Science and Software EngineeringThe University of MelbourneAustralia

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