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Proceedings Paper

Evolutionary programming for goal-driven dynamic planning
Author(s): James M. Vaccaro; Clark C. Guest; David O. Ross
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Paper Abstract

Many complex artificial intelligence (IA) problems are goal- driven in nature and the opportunity exists to realize the benefits of a goal-oriented solution. In many cases, such as in command and control, a goal-oriented approach may be the only option. One of many appropriate applications for such an approach is War Gaming. War Gaming is an important tool for command and control because it provides a set of alternative courses of actions so that military leaders can contemplate their next move in the battlefield. For instance, when making decisions that save lives, it is necessary to completely understand the consequences of a given order. A goal-oriented approach provides a slowly evolving tractably reasoned solution that inherently follows one of the principles of war: namely concentration on the objective. Future decision-making will depend not only on the battlefield, but also on a virtual world where military leaders can wage wars and determine their options by playing computer war games much like the real world. The problem with these games is that the built-in AI does not learn nor adapt and many times cheats, because the intelligent player has access to all the information, while the user has access to limited information provided on a display. These games are written for the purpose of entertainment and actions are calculated a priori and off-line, and are made prior or during their development. With these games getting more sophisticated in structure and less domain specific in scope, there needs to be a more general intelligent player that can adapt and learn in case the battlefield situations or the rules of engagement change. One such war game that might be considered is Risk. Risk incorporates the principles of war, is a top-down scalable model, and provides a good application for testing a variety of goal- oriented AI approaches. By integrating a goal-oriented hybrid approach, one can develop a program that plays the Risk game effectively and move one step closer to solving more difficult real-world AI problems. Using a hybrid approach that includes adaptation via evolutionary computation for the intelligent planning of a Risk player's turn provides better dynamic intelligent planning than more uniform approaches.

Paper Details

Date Published: 11 March 2002
PDF: 12 pages
Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); doi: 10.1117/12.458717
Show Author Affiliations
James M. Vaccaro, Orincon Corp. and Univ. of California/San Diego and Air Force Research Lab. (United States)
Clark C. Guest, Univ. of California/San Diego (United States)
David O. Ross, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 4739:
Applications and Science of Computational Intelligence V
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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