Mission Command Analysis Using Monte Carlo Tree Search
Abstract
In this project examine applications of Monte Carlo tree search, an artificial intelligence algorithm, in military simulation environments and assignment and scheduling problems with the goal of enhancing mission command analysis capabilities. We provide a review of recent literature on Monte Carlo tree search methods and then develop two algorithms that adapt the Monte Carlo tree search algorithm, traditionally applied to deterministic, fully observable games, to military simulations, which are typically stochastic and partially observable in nature. We develop, test, and comment on the results of two prototype implementations: one in a simple simulation environment with the objective of conserving friendly strength while depleting opposing forces, and the other focused on producing an optimal or near optimal assignment and schedule of aerial platforms against a set of missions with known values. Finally, we conclude by making recommendations for future implementations and applications in the COMBATXXI and JDAFS simulation environments, and suggest ways of addressing some of the computation challenges associated with Monte Carlo tree search and recursive simulation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 14, 2013
- Accession Number
- ADA586801
Entities
People
- Arnie Buss
- Christian J. Darken
- Christopher Marks
- Jonathan Alt
- Kyle Lin
Organizations
- United States Army Training and Doctrine Command