A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Monte Carlo Tree Search Applied to a Modified Pursuit/Evasion Scotland Yard Game with Rendezvous Spaceflight Operation Applications

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based reinforcement learning model known for using limited domain knowledge to push favorable results. Monte Carlo agents have been used in a multitude of imperfect domain knowledge games. One such game was in which Monte Carlo agents were produced and studied in an imperfect domain game for pursuit-evasion tactics is Scotland Yard. This thesis continues the Monte Carlo agents previously produced by Mark Winands and Pim Nijssen and applied to Scotland Yard. In the research presented here, the rules for Scotland Yard are analyzed and presented in an expansion that partially accounts for spaceflight dynamics in order to study the agents within a simplified model, while having some foundation for use within space environments. Results show promise for the use of Monte- Carlo agents in pursuit/evasion autonomous space scenarios while also illuminating some major challenges for future work in more realistic three-dimensional space environments