Preference Learning for Move Prediction and Evaluation Function Approximation in Othello

This paper investigates the use of preference learning as an approach to move prediction and evaluation function approximation, using the game of Othello as a test domain. Using the same sets of features, we compare our approach with least squares temporal difference learning, direct classification, and with the Bradley-Terry model, fitted using minorization-maximization (MM). The results show that the exact way in which preference learning is applied is critical to achieving high performance. Best results were obtained using a combination of board inversion and pair-wise preference learning. This combination significantly outperformed the others under test, both in terms of move prediction accuracy, and in the level of play achieved when using the learned evaluation function as a move selector during game play

Evolutionary Artificial Neural Network Weight Tuning to Optimize Decision Making for an Abstract Game

Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors’ performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a distributed environment. While pLoGANN proved to be designed efficiently, it failed to produce a competitive Lines of Action player, shedding light on the difficulty of developing a neural network to model such a large and complex solution space

A learning framework for zero-knowledge game playing agents

The subjects of perfect information games, machine learning and computational intelligence combine in an experiment that investigates a method to build the skill of a game-playing agent from zero game knowledge. The skill of a playing agent is determined by two aspects, the first is the quantity and quality of the knowledge it uses and the second aspect is its search capacity. This thesis introduces a novel representation language that combines symbols and numeric elements to capture game knowledge. Insofar search is concerned; an extension to an existing knowledge-based search method is developed. Empirical tests show an improvement over alpha-beta, especially in learning conditions where the knowledge may be weak. Current machine learning techniques as applied to game agents is reviewed. From these techniques a learning framework is established. The data-mining algorithm, ID3, and the computational intelligence technique, Particle Swarm Optimisation (PSO), form the key learning components of this framework. The classification trees produced by ID3 are subjected to new post-pruning processes specifically defined for the mentioned representation language. Different combinations of these pruning processes are tested and a dominant combination is chosen for use in the learning framework. As an extension to PSO, tournaments are introduced as a relative fitness function. A variety of alternative tournament methods are described and some experiments are conducted to evaluate these. The final design decisions are incorporated into the learning frame-work configuration, and learning experiments are conducted on Checkers and some variations of Checkers. These experiments show that learning has occurred, but also highlights the need for further development and experimentation. Some ideas in this regard conclude the thesis.Dissertation (MSc)--University of Pretoria, 2007.Computer ScienceMScUnrestricte

A Multi-Agent Approach to the Game of Go Using Genetic Algorithms

This is the published version. Copyright De GruyterMany researchers have written or attempted to write programs that play the ancient Chinese board game called Go. Although some programs play the game quite well compared with beginners, few play extremely well, and none of the best programs rely on soft computing artificial intelligence techniques like genetic algorithms or neural networks. This paper explores the advantages and possibilities of using genetic algorithms to evolve a multiagent Go player. We show that although individual agents may play poorly, collectively the agents working together play the game significantly better

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

Evaluation Functions in General Game Playing

While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.:Introduction Game Playing Evaluation Functions I - Aggregation Evaluation Functions II - Features General Evaluation Related Work Discussio

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