A Framework for Goal-Based Semantic Compensation in Agent Systems

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.