Monte-Carlo tree search with heuristic knowledge: A novel way in solving capturing and life and death problems in Go

Monte-Carlo (MC) tree search is a new research field. Its effectiveness in searching large state spaces, such as the Go game tree, is well recognized in the computer Go community. Go domain- specific heuristics and techniques as well as domain-independent heuristics and techniques are sys- tematically investigated in the context of the MC tree search in this dissertation. The search extensions based on these heuristics and techniques can significantly improve the effectiveness and efficiency of the MC tree search. Two major areas of investigation are addressed in this dissertation research: I. The identification and use of the effective heuristic knowledge in guiding the MC simulations, II. The extension of the MC tree search algorithm with heuristics. Go, the most challenging board game to the machine, serves as the test bed. The effectiveness of the MC tree search extensions is demonstrated through the performances of Go tactic problem solvers using these techniques. The main contributions of this dissertation include: 1. A heuristics based Monte-Carlo tactic tree search framework is proposed to extend the standard Monte-Carlo tree search. 2. (Go) Knowledge based heuristics are systematically investigated to improve the Monte-Carlo tactic tree search. 3. Pattern learning is demonstrated as effective in improving the Monte-Carlo tactic tree search. 4. Domain knowledge independent tree search enhancements are shown as effective in improving the Monte-Carlo tactic tree search performances. 5. A strong Go Tactic solver based on proposed algorithms outperforms traditional game tree search algorithms. The techniques developed in this dissertation research can benefit other game domains and ap- plication fields

Forward Pruning and Other Heuristic Search Techniques in Tsume Go

GoTools is the name of a computer program to analyse life & death fights in the game of go. First, a motivation for programing go is given. General aspects of the heuristic search performed in GoTools are discussed. The absolute strength and the effect of forward tree pruning are tested in a competition between GoTools and dan level human players. It is found that with a speed up of a factor of 30 the program still finds for 70% of the problems of a test set the correct first move. A suggestion is made to compare intelligence between contestants (in solving fully enclosed life/death problems) by comparing their relative increase in the time to solve problems with the increase of the difficulty of the problems. The rules of go are explained in the shortest possible form in the appendix. Introduction The game of go is a 4500 yr. old Asian board game in which two persons put stones on a board to gain territory and take prisoners. Despite having very few, simple rules and the substantial..

Spatial-temporal reasoning applications of computational intelligence in the game of Go and computer networks

Spatial-temporal reasoning is the ability to reason with spatial images or information about space over time. In this dissertation, computational intelligence techniques are applied to computer Go and computer network applications. Among four experiments, the first three are related to the game of Go, and the last one concerns the routing problem in computer networks. The first experiment represents the first training of a modified cellular simultaneous recurrent network (CSRN) trained with cellular particle swarm optimization (PSO). Another contribution is the development of a comprehensive theoretical study of a 2x2 Go research platform with a certified 5 dan Go expert. The proposed architecture successfully trains a 2x2 game tree. The contribution of the second experiment is the development of a computational intelligence algorithm calledcollective cooperative learning (CCL). CCL learns the group size of Go stones on a Go board with zero knowledge by communicating only with the immediate neighbors. An analysis determines the lower bound of a design parameter that guarantees a solution. The contribution of the third experiment is the proposal of a unified system architecture for a Go robot. A prototype Go robot is implemented for the first time in the literature. The last experiment tackles a disruption-tolerant routing problem for a network suffering from link disruption. This experiment represents the first time that the disruption-tolerant routing problem has been formulated with a Markov Decision Process. In addition, the packet delivery rate has been improved under a range of link disruption levels via a reinforcement learning approach --Abstract, page iv