| Summary: | 博士 === 國立東華大學 === 資訊工程學系 === 99 === Recently, Monte Carlo Tree Search (MCTS) has become a well-known game search method, and has been successfully applied to many games. This method performs well in solving search trees with numerous branches, such as Go, Havannah, etc.
Connect6 is a game involving a search tree with numerous branches, and it is also one of the sudden-death games. This thesis thus proposes a new MCTS variant related to Connect6, called two-stage MCTS. In the search architecture, the first stage focuses on Threat Space Search (TSS), which is designed to solve the sudden-death problem. For the double-threat TSS in Connect6, this thesis proposes an algorithm called Iterative Threat Space Search (ITSS) that combines normal TSS with Conservative Threat Space Search (CTSS). The second stage uses MCTS to estimate the game-theoretic value of the initial position. This stage aims at finding the most promising move.
Except for search algorithms, efficiency is the key point to Connect6. Many researches use bits to encode the board states and the related bitwise computing to model the real problems. In this theme, the research was designed as a case study to provide insight to the issue of Bitboard. Therefore, this thesis uses the concept: Bitboard, to parse the data structure in Connect6 and to analysis the related bitwise computing to accelerate the transition of board states and to gain information. The result shows that these methods are more efficient and be able to improve the search performance of a Connect6 program.
Furthermore, according to Connect6 heuristic knowledge, this thesis uses relevance-zone search to accelerate identifying winning and losing moves, and this method can save a lot of time on searching. The experimental result shows that two-stage MCTS is considerably more efficient than traditional MCTS on those positions with TSS solution in Connect6.
|
|---|
