Study On Problems For Computer Go Based On Cognitive Science

Cognitive science is a symbolic emerging research field of world science in the 20th century. As an exploratory subject focusing on the working of human brain, it has aroused wide attention from all around the world. Computer Go has become an important tool in cognitive scientific research due to its visual abstractness and complexity.In recent years, researchers have been solving Go game problems by using the algorithms including no Go knowledge such as MC, UCT, AMAF, etc. This paper presents a series of Go game resolutions based on the simulation of human players' thinking method and quantization of the general judgment by analyzing the knowledge structure, expression style and thinking mode of Go players. It is not only helpful for improving the level of the Go program, but also for promoting the cognitive ability of the human beings, which has innovative significance and utilitarian value in the research and development of cognitive science.The innovative thinking is as follows:1. Set levels for the groups'liberties. According to the levels, the groups'safeness can be judged, the target can be fixed and the candidate moves can be generated and sorted. Search the candidate moves in sequence by MTD(f) algorithm to find the best capture move. The result shows the method has good effect and can be applied in the real game of computer Go, which lays groundwork for defining effective number of stones when calculating the strong group's value.2. Quantize the value of the strong groups. Instead of the traditional way of calculating the controlled points, this paper presents a theory that the influence of the strong groups resembles the diffusion of control probability in the two-dimensional space and the sum of the control probability of the unoccupied points equals to the value of the strong group. An influence function and a mathematical model are established to prove it. We attempt to divide the value of the strong groups into the basic value and the added value, and then further adjust the judgments of the value of the strong groups and simulate the sense of human players toward the strong groups. Finally, the simple genetic algorithm is used to optimize the model's parameter by level and the strong group quantization models of different Go levels are obtained.The experimental result shows the model is of high accuracy so that it can be applied in starting game, middle game and end game of the computer Go and provide the basis for the static evaluation program and founds the basis for the subsequent program design of quantizing the game situation.3. Based on the above model, a quantization method is presented to evaluate the situation and winning probability is used to show the results of quantization. The parameters are optimized by simulating human player's thinking method, taking the leading points and game process as the calculating basis of winning probability and combining species compete-die out algorithms. The parameters in the model can be adjusted according to different Go levels so that the model can successfully cope with various Go levels, which boasts good portability and universality and obtains satisfying results in the experiment.The method which establishes model by Go knowledge and defines parameters by genetic algorithm can also be applied in other computer games. Thus it lays the foundation for the subsequent programs to select and define the best move.4. A middle game strategy in computer Go including methods of generating, evaluating and defining moves is presented. The candidate moves are initially evaluated by assessing the values of territory, strategy, shape and influence. The moves are divided into attacking and defending moves according to the difference of the purposes. Combining with winning probability, the strength of attack and defense is calculated and the weight value is adjusted, and then the weight values of attacking and defending moves are dynamically adjusted so that the best move is obtained. This method does not only simulate the thinking process of human players but also makes full play of the calculating advantage of the computer to combine the dynamic analysis, static search and knowledge base, which shows the intelligence of the computer.5. CognitiveGo, a computer Go game system is developed by integrating the above. CognitiveGo searches some possible moves according to the pattern library before playing each move, and then make further judgments about the territory, strong groups and group strength of both sides according to its Go level. After that, it adjusts the subject direction of the next move by the judgment result. During the process, the pattern library has influence on the recommendation of the candidate moves while the transformation of the subject is influence by the positional analysis.In conclusion, this paper researches on the computer Go problem mainly by establishing the model based on simulating the thinking process of the human players. The research method and results has academic and practical value in promoting computer game intelligence and the development of cognitive science.