Intentional learning agent architecture |
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Authors: | Budhitama Subagdja Liz Sonenberg Iyad Rahwan |
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Affiliation: | (1) Intelligent Systems Centre, Nanyang Technological University, Singapore, Singapore;(2) Department of Information Systems, University of Melbourne, Melbourne, Australia;(3) Faculty of Informatics, The British University in Dubai, Knowledge Village, Dubai;(4) School of Informatics (Fellow), University of Edinburgh, Edinburgh, UK |
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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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Keywords: | Autonomous agents BDI agent architecture Machine learning Plans Abduction |
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