Mixed-Initiative Reasoning for Integrated Domain Modeling, Learning and Problem Solving

Mihai Boicu and Gheorghe Tecuci, George Mason University

This paper introduces a powerful and flexible mixed-initiative plausible reasoner that allows the expert to train an agent in a variety of ways, and in as natural a manner as possible, similar to the way the expert would train a human apprentice. The plausible reasoner distinguishes between four types of increasingly complex problem solving situations, routine, innovative, inventive and creative, providing a basis for an integration of the domain modeling, learning and problem solving processes involved in developing the knowledge base of the agent.


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