AAAI Publications, Twenty-Fourth International FLAIRS Conference

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Active and Interactive Discovery of Goal Selection Knowledge
Jay Powell, Matthew Molineaux, David William Aha

Last modified: 2011-03-20


If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge.

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