Learning through Interactive Behavior Specifications

Tolga Könik, Douglas J. Pearson, and John E. Laird

We describe a learning from diagrammatic behavior specifications approach, where the task-performance knowledge of a human expert is transferred to an agent program using abstract behavior scenarios that the expert and the agent program interactively specify. The diagrammatic interface serves as a communication medium between the expert and the agent program to share knowledge during behavior specification. A relational learning by observation component interprets these scenarios in the context of background knowledge and expert annotations to learn first-order rules that represent the task-performance knowledge for an improved agent program.

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