AI for General Strategy Game Playing

Abstract

List of Figures vii List of Tables ix Acknowledgments xi AI for General Strategy Game Playing 13 10.1 Introduction 13 10.1.1 Strategy games 13 10.1.2 AI for board games 15 10.1.3 AI for strategy games 16 10.2 Research questions and methodology 18 10.2.1 The SGDL and its framework 19 10.3 The game, the agents and the assigner 21 10.3.1 Implementation details 24 10.4 Agents 24 10.4.1 Random action selection 25 10.4.2 Finite-state machine 26 10.4.3 Neuroevolution of augmenting topologies 26 v vi CONTENTS 10.4.4 MinMax 10.4.5 Monte Carlo Tree Search 10.4.6 Potential fields 10.4.7 Classifier systems 10.5 Results of agent versus agent testing 10.6 Results of human play testing 10.7 Discussion 10.8 Conclusions References LIST OF FIGURES 10.1 A typical screenshot from our example game "RockWars" 10.2 The Commander framework used for the agents in the study. 10.3 Client-server system used for training and experimentation. 10.4 Finite-state automata of the SemiRandom agent units 10.5 Finite-state automaton of the FSM agent's units 10.6 Action tree used to find MultiActions 10.7 Action tree illustrating the use of buildings. 10.8 Summary of agent versus agent results 10.9 Summary of human play results vii LIST OF TABLES 10.1 Unit properties for SGDL models 10.2 Agents in the study 10.3 Summary of agent versus agent results 10.4 Standard deviantions of agent versus agent results 10.5 Summary of human play results ix ACKNOWLEDGMENTS For more technical details, please refer to the first two authors' joint masters thesis [1], or Tobias Mahlmann's PhD thesis including the detailed description of the Strategy Games Description Language [2]. Both of these are available online at http://game.itu.dk/sgdl. This research was supported in part by the Danish Research Agency project "AGameComIn" (274-09-0083). xi CHAPTER 10 AI FOR GENERAL STRATEGY GAME PLAYING 1 For brevity, we will refer to computer strategy games as strategy games in the following sections. AI for General Strategy Game Playing.

Malaria Detection Using Advanced Deep Learning Architecture

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