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Intentional learning agent architecture

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Abstract

Dealing with changing situations is a major issue in building agent systems. When the time is limited, knowledge is unreliable, and resources are scarce, the issue becomes more challenging. The BDI (Belief-Desire-Intention) agent architecture provides a model for building agents that addresses that issue. The model can be used to build intentional agents that are able to reason based on explicit mental attitudes, while behaving reactively in changing circumstances. However, despite the reactive and deliberative features, a classical BDI agent is not capable of learning. Plans as recipes that guide the activities of the agent are assumed to be static. In this paper, an architecture for an intentional learning agent is presented. The architecture is an extension of the BDI architecture in which the learning process is explicitly described as plans. Learning plans are meta-level plans which allow the agent to introspectively monitor its mental states and update other plans at run time. In order to acquire the intricate structure of a plan, a process pattern called manipulative abduction is encoded as a learning plan. This work advances the state of the art by combining the strengths of learning and BDI agent frameworks in a rich language for describing deliberation processes and reactive execution. It enables domain experts to specify learning processes and strategies explicitly, while allowing the agent to benefit from procedural domain knowledge expressed in plans.

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Authors and Affiliations

  1. Intelligent Systems Centre, Nanyang Technological University, Singapore, Singapore

    Budhitama Subagdja

  2. Department of Information Systems, University of Melbourne, Melbourne, Australia

    Liz Sonenberg

  3. Faculty of Informatics, The British University in Dubai, Knowledge Village, Dubai

    Iyad Rahwan

  4. School of Informatics (Fellow), University of Edinburgh, Edinburgh, UK

    Iyad Rahwan

Authors
  1. Budhitama Subagdja
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  2. Liz Sonenberg
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  3. Iyad Rahwan
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Correspondence to Budhitama Subagdja.

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Subagdja, B., Sonenberg, L. & Rahwan, I. Intentional learning agent architecture. Auton Agent Multi-Agent Syst 18, 417–470 (2009). https://doi.org/10.1007/s10458-008-9066-5

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