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29th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Paiva Tomaz, Lidia Bononi Silva Julia, Rita Maria Prandini Faria, Matheus Prado Univ Fed Uberlandia Comp Sci Dept Uberlandia MG Brazil

ISBN: (纸本)9781538638767

This paper has two main goals. Its first purpose is to create, in a distributed memory architecture, the Checkers agent APHID-Draughts, which is a player system designed to operate in a high performance environment. This system counts on an unsupervised learning process, where the decisions are made by means of distributed asynchronous version of the alpha-beta algorithm, known as APHID (Asynchronous Parallel Hierarchical Iterative Deepening). The second goal of this paper is to compare the performance in distributed memory architectures between unsupervised player agents operating according to one of the following alpha-beta parallelism approaches: asynchronous or synchronous. This second goal is performed through tournaments in which the agent APHID-Draughts proposed herein faces off with D-VisionDraughts, a Checkers player agent operating with the synchronous distributed version of alpha-beta known as YBWC (Young Brothers Wait Concept). Noteworthy here is that APHID-Draughts and D-VisionDraughts both consist of Multilayer Perceptron Neural Networks that learn through Temporal Difference Methods TD(lambda). Thus, the only difference between these agents refers to the decision-making strategy, since the former uses the asynchronous algorithm APHID and the latter uses the synchronous version YBWC. The results obtained confirm the theoretically expected assumption that asynchronous approaches are more suitable for operating in distributed memory architectures than those of a synchronous nature.

关键词： Asynchronous Parallel Hierarchical Iterative Deepening Young Brothers Wait Concept alpha-beta algorithm Unsupervised Agents for Checkers Machine Learning

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Research on Search algorithm in Surakarta Chess Game System

International Core Journal of Engineering

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International Core Journal of Engineering 2023年第7期9卷 286-293页

作者： Tao Zhang Yefeng Jiang Bowen Li

This paper introduces Surakarta, an important branch in the field of computer games, and discusses two search algorithms used in this game: alpha-beta pruning algorithm and UCT algorithm. The search algorithm plays a central role in the game system and affects the performance and efficiency of the system. alpha-beta pruning algorithm is an optimized and improved maxima-minimum algorithm, which reduces unnecessary search branches and improves search efficiency through pruning principles. The UCT algorithm is a game tree search algorithm based on the Monte Carlo method, which estimates the true value of the action through random simulation and tree building and expansion, and adjusts the strategy to the best priority strategy. The application of these two algorithms in the Surakarta chess game system can improve the performance and accuracy of computer games. Through the research and application of Surakarta chess, it can not only promote the development of artificial intelligence in chess games, but also provide enlightenment for problem solving in other fields.

关键词： Surakarta Chess alpha-beta algorithm UCT algorithm Monte Carlo algorithm

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Research on integrated computer game algorithm for dots and boxes

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 601-606页

作者： Li, Shuqin Zhang, Yipeng Ding, Meng Dai, Pengcheng Beijing Informat Sci & Technol Univ Comp Acad Beijing Peoples R China Percept & Computat Intelligence Joint Lab 35 North Fourth Ring Rd Beijing Peoples R China

Situation assessment and search are two key problems in computer game research. In general, as the game progresses, the difficulty of evaluating the situation of the game is significantly reduced, and the accuracy of the evaluation is significantly increased. Based on the famous chess game, this article proposes and implements a new scheme that combines the Monte Carlo tree search algorithm, the alpha-beta algorithm and the model based on the deep convolution neural network (CNN) to solve the computer game problem. This article first proposes a deep convolutional neural network model based on dots and boxes, including deep value network and deep strategy network, focusing on situation assessment and strategy recommendation, respectively. Then, using the Monte Carlo Tree Search (MCTS) algorithm as a framework, deep value network integrated MCTS algorithm and deep strategy network integrated MCTS algorithm are proposed. In both integrated models, alpha-beta complete search is used to truncate the Monte Carlo simulation process and improve simulation efficiency. Through competition with human players, the results show that the two integrated algorithm game systems have reached much higher intelligence level than ordinary humans in solving the problem of dots and boxes.

关键词： C++ language tree searching Monte Carlo methods artificial intelligence neural nets computer games game theory research on integrated computer game algorithm dots computer game research game progresses famous chess game Monte Carlo tree search algorithm alpha-beta algorithm deep convolution neural network computer game problem deep convolutional neural network model deep value network situation assessment strategy recommendation Monte Carlo Tree Search algorithm deep strategy network integrated MCTS algorithm integrated models alpha-beta complete search Monte Carlo simulation process integrated algorithm game systems

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Building an Arimaa-playing program using Basic Search algorithms 2

Building an Arimaa-playing program using Basic Search Algori...

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2nd International Conference on Information Systems and Computer Science (INCISCOS)

作者： Rubio, Jose-Miguel Salas, Juan Univ Tecnol Chile INACAP Dept Comp Sci Santiago Chile Univ Tecnol Chile INACAP IT Acad Santiago Chile

ISBN: (纸本)9781538626443

This paper details the development of an Arimaa program prototype, a board game with very simple and intuitive rules to humans, but which are very complex for computers. In order to approach this problem, the game was analyzed, studying its official history, how it is played, its rules, and the complications presented when programming it. An approach to achieve this goal was then designed, incorporating solutions that have been developed for chess, defining a numerical representation of the data, and introducing a movement generator, a search function, a Minimax algorithm with alpha-beta pruning, and function evaluation, before finally proceeding with the implementation and evaluation of a prototype.

关键词： arimaa challenge minimax algorithm alpha-beta algorithm artificial intelligence

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The Application of Optimized Artificial Intelligence algorithm in Chinese Chess

The Application of Optimized Artificial Intelligence Algorit...

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2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology(FMSMT 2017)

作者： Yingying Qiu Guangxi Economic and Trade Vocational and Technical College

ISBN: (纸本)9781510840232

In the field of artificial intelligence, the rule of Chinese chess is different from International chess and chess and it decided that the Chinese chess artificial intelligence algorithm has its particularity. Based on the characteristics of Chinese chess, this paper analyzes the computer artificial intelligence algorithm of Chinese chess, such as game tree algorithm, historical heuristic algorithm and alpha-beta algorithm, and puts forward the recent activity method, dynamic sub-force method and player style method. The computational analysis shows that the improved new algorithm has a significant improvement in efficiency and speed.

关键词： Chinese Chess Artificial Intelligence algorithm Game Tree algorithm Historical Heuristic algorithm alpha-beta algorithm Recent Activity Method Dynamic Sub-Force Method Player Style Method

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Reduced-complexity scheme using alpha-beta filtering for location tracking

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IET COMMUNICATIONS 2011年第13期5卷 1806-1813页

作者： Chiou, Y. -S. Wang, C. -L. Yeh, S. -C. Natl Tsing Hua Univ Inst Commun Engn Hsinchu 30013 Taiwan Natl Cent Univ Ctr Space & Remote Sensing Res Jhongli 32001 Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu 30013 Taiwan Ming Chuan Univ Dept Comp & Commun Engn Tao Yuan 33324 Taiwan

This study presents an efficient location tracking algorithm to reduce the computational complexity of the conventional Kalman filtering (KF) algorithm. In the proposed training and tracking scheme, the authors replace the decision mode of the KF algorithm with an alpha-beta (alpha-beta) algorithm to avoid repeatedly calculating the Kalman gain. After the mode with alpha-beta - tracking, the exact information of the state and measurement noise parameters used in the KF algorithm is not required. Using the inherent fixed-coefficient feature of alpha-beta filtering, the location information between the prediction phase and correction phase is efficiently cycled, thus simplifying implementation of the KF approach. Under a stationary environment, numerical simulations show that the proposed training and tracking approach not only can achieve the location accuracy close to the KF scheme but has much lower computational complexity.

关键词： Computational complexity Digital signal processing Filtering methods in signal processing location tracking computational complexity alpha-beta algorithm alpha-beta filtering conventional Kalman filtering reduced-complexity scheme target tracking Kalman filters

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