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This paper presents the results of a multi-faceted research and development effort that synergistically integrates artificial intelligence research with military strategy research and practical deployment of agents into education. It describes recent advances in the Disciple approach to agent development by subject matter experts with limited assistance(More)
This paper introduces the concept of learning agent shell as a new class of tools for rapid development of practical endto-end knowledge-based agents, by domain experts, with limited assistance from knowledge engineers. A learning agent shell consists of a learning and knowledge acquisition engine as well as an inference engine and supports building an(More)
methodology for building knowledge bases and agents and their innovative application to the development of a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency’s High-Performance Knowledge Bases Program. The learning agent shell includes a general problemsolving engine and a general(More)
Over the years we have developed the Disciple theory, methodology, and family of tools for building knowledge-based agents. This approach consists of developing an agent shell that can be taught directly by a subject matter expert in a way that resembles how the expert would teach a human apprentice when solving problems in cooperation. This paper presents(More)
This paper addresses the problem of improving the representation space in a rule-based intelligent system, through exception-based learning. Such a system generally learns rules containing exceptions because its representation language is incomplete. However, these exceptions suggest what may be missing from the system's ontology, which is the basis of the(More)
Intelligence analysts encounter a wide variety of items of evidence provided by an array of different sources. Some of these sources are human assets or informants; other sources are sensing devices of various kinds. Of great concern is the extent to which the events revealed in these evidence items can be believed. There is always the possibility that(More)
Mixed-initiative assistants are agents that interact seamlessly with humans to extend their problem solving capabilities or provide new capabilities. Developing such agents requires the synergistic integration of many areas of AI, including knowledge representation, problem solving and planning, knowledge acquisition and learning, multi-agent systems,(More)
This paper presents a successful knowledge acquisition experiment in which subject matter experts that did not have any prior knowledge engineering experience succeeded to teach the Disciple-COA agent how to critique courses of action, a challenge problem addressed by the DARPA’s High Performance Knowledge Bases program. We first present the COA challenge(More)