DOI:
Abstract:
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.