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Aberdeen, D., Thi'ebaux, S., Zhang, L., & others. 2004, Decision-theoretic military operations planning.. Paper presented at ICAPS. 
Resource type: Proceedings Article
BibTeX citation key: Aberdeen2004
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Categories: Artificial Intelligence, Computer Science, Decision Theory, Engineering, General, Geopolitical, Mathematics, Military Science
Subcategories: Analytics, Australia, Autonomous systems, Command and control, Decision making, Machine intelligence, Machine learning, Military research, Psychology of human-AI interaction, Robotics
Creators: Aberdeen, others, Thi'ebaux, Zhang
Collection: ICAPS
Military operations planning involves concurrent actions, resource assignment, and conflicting costs. Individual tasks sometimes fail with a known probability, promoting a decision-theoretic approach. The planner must choose between multiple tasks that achieve similar outcomes but have different costs. The military domain is particularly suited to automated methods because hundreds of tasks, specified by many planning staff, need to be quickly and robustly coordinated. The authors are not aware of any previous planners that handle all characteristics of the operations planning domain in a single package. This paper shows that problems with such features can be successfully approached by realtime heuristic search algorithms, operating on a formulation of the problem as a Markov decision process. Novel automatically generated heuristics, and classic caching methods, allow problems of interesting sizes to be handled. Results are presented on data provided by the Australian Defence Science and Technology Organisation.
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