The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management . This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time.

The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time.

The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. The workshop organizer, Pete Bonasso, sent a point paper to a number of invited presenters giving his opinion of what AI could and could not do for battle management. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time. Finally, there are several areas -- cooperating systems and terrain reasoning, for example -- where, given some impetus, AI might be able to provide help in the near future.

James Peters, coauthor of "A Knowledge-Based Model of Audit Risk," is an assistant professor in the Department of Accounting, College of Business Administration, University of Oregon. Glenn D. Rennels coauthor of "Prose Generation from Expert Systems: An Applied Computational Linguistics Thomas Arcidiacono, the author of the review of An Artificial Intelligence Approach, " is a research affiliate in Approach to Legal Reasoning, is affiliated with the Artificial Intelligence Laboratory, the Medical Information Sciences Program, the New York Institute of Technology, Sunburst Center 203, Central Edwina L. Rissland, author of "Artificial Intelligence and Legal Reasoning: R. Peter Bonasso, author of "An Hermann Kaindl, author of "Minimaxing: A Discussion of the Field and Assessment of What AI Can Do for Theory and Practice", is a Gardner's Book," is an associate professor Battle Management--A Report of the software engineer in the position of of Computer and Information First AAAI Workshop on AI Applications "Gruppenleiter" at Siemens AG Science at the University of Massachusetts to Battle Management" is the osterreich, Program and System Engineering at Amherst and lecturer on department head of the Artificial Since 1984, he has been a lecturer law at the Harvard Law School. Operations division, 7525 Colshire research interests include planning Drive, Mclean, VA 22102. Vasant Dhar, coauthor of "A Knowledge-Based Model of Audit Risk," is Model of Audit Risk," is Peat Marwick Professor of Accounting, Kenneth D. Forbus is an assistant professor Perry Miller, coauthor of "Prose Generation of computer science at the University from Expert Systems: An Call toU-free 800-521-3044 Or mail inquiry to: University Microfilms International. Forbus's research interests Program, Yale University include qualitative reasoning, inference School of Medicine, 333 Cedar Street, engine design, analogical reasoning P.O.