Rapid Development of a High Performance Knowledge Base for Course of Action Critiquing

Gheorghe Tecuci, Mihai Boicu, Dorin Marcu, Michael Bowman, Florin Ciucu, and Cristian Levcovici, George Mason University

This paper presents a practical learning-based methodology and agent shell for building knowledge bases and knowledge-based agents, and their innovative application to the development of a critiquing agent for military courses of action, a challenge problem set by DARPA’s High Performance Knowledge Bases program. The agent shell consists of an integrated set of knowledge acquisition, learning and problem solving modules for a generic knowledge base structured into two main components: an ontology that defines the concepts from a specific application domain, and a set of task reduction rules expressed with these concepts. The rapid development of the COA critiquing agent was done by importing an initial ontology from CYC and by teaching the agent to perform its tasks in a way that resembles how an expert would teach a human apprentice when solving problems in cooperation. The methodology, the agent shell, and the developed critiquer were evaluated in several intensive studies, and demonstrated very good results.


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