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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)
—Intelligence analysis requires the development of arguments that link evidence to hypotheses by establishing and fusing the relevance, believability and inferential force or weight of a wide variety of items of evidence of different types. This paper presents several substance-blind classifications of evidence which are based on these inferential(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 results on developing a general intelligence analysis ontology which is part of the knowledge base of Disciple-LTA, a unique and complex cognitive assistant for evidence-based hypothesis analysis that helps an intelligence analyst cope with many of the complexities of intelligence analysis. It introduces the cognitive assistant and(More)
— This paper presents an approach to rapid development of virtual planning experts that can collaborate to develop plans of action requiring expertise from multiple domains. The approach is implemented into a new type of software tool, called Disciple-VPT, which includes an exten-sible library of virtual planning experts from different domains. Teams of(More)
This paper presents a learning-based representation of knowledge which is at the basis of the family of Disciple learning agents. It introduces a representation for concepts, generalization and specialization rules, different types of generalizations and specializations, and the representation of the main elements of a knowledge base, including partially(More)
This paper discusses several critical capabilities of the Disciple-LTA system for complex problem-solving and decision-making, including a transparent and easy to understand reasoning process, a flexible and natural collaboration with the user, and the use of what-if scenarios to cope with incomplete and uncertain information. They allow the user to act as(More)