Includes bibliographical references and index.

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Algorithms and Networking for Computer Games; Contents; List of Figures; List of Tables; List of Algorithms; Preface; Acknowledgements; 1 Introduction; 1.1 Anatomy of Computer Games; 1.2 Game Development; 1.2.1 Phases of development; 1.2.2 Documentation; 1.2.3 Other considerations; 1.3 Synthetic Players; 1.3.1 Humanness; 1.3.2 Stance; 1.4 Multiplaying; 1.5 Interactive Storytelling; 1.5.1 Approaches; 1.5.2 Storytelling in games; 1.6 Outline of the Book; 1.6.1 Algorithms; 1.6.2 Networking; 1.7 Summary; Exercises; Part I Algorithms; 2 Random Numbers; 2.1 Linear Congruential Method

2.1.1 Choice of parameters2.1.2 Testing the randomness; 2.1.3 Using the generators; 2.2 Discrete Finite Distributions; 2.3 Random Shuffling; 2.4 Summary; Exercises; 3 Noise; 3.1 Applying Noise; 3.2 Origin of Noise; 3.3 Visualization; 3.4 Interpolation; 3.4.1 Utility routines for value conversions; 3.4.2 Interpolation in a single parameter; 3.4.3 Interpolation in two parameters; 3.5 Composition of Noise; 3.6 Periodic Noise; 3.7 Perlin Noise; 3.8 Worley Noise; 3.9 Summary; Exercises; 4 Procedural Generation; 4.1 Terrain Generation; 4.2 Maze Algorithms; 4.2.1 Depth-first algorithm

4.2.2 Randomized Kruskal's algorithm4.2.3 Randomized Prim's algorithm; 4.3 L-Systems; 4.3.1 Examples; 4.3.2 City generation; 4.4 Hierarchical Universe Generation; 4.5 Summary; Exercises; 5 Tournaments; 5.1 Rank Adjustment Tournaments; 5.2 Elimination Tournaments; 5.3 Scoring Tournaments; 5.4 Summary; Exercises; 6 Game Trees; 6.1 Minimax; 6.1.1 Analysis; 6.1.2 Partial minimax; 6.2 Alpha-Beta Pruning; 6.2.1 Analysis; 6.2.2 Principal variation search; 6.3 Monte Carlo Tree Search; 6.4 Games of Chance; 6.5 Summary; Exercises; 7 Path Finding; 7.1 Discretization of the Game World; 7.1.1 Grid

7.1.2 Navigation mesh7.2 Finding the Minimum Path; 7.2.1 Evaluation function; 7.2.2 Properties; 7.2.3 Algorithm A*; 7.3 Realizing the Movement; 7.4 Summary; Exercises; 8 Group Movement; 8.1 Flocking; 8.2 Formations; 8.2.1 Coordinating formations; 8.2.2 Behaviour-based steering; 8.2.3 Fuzzy logic control; 8.2.4 Mass-spring systems; 8.3 Summary; Exercises; 9 Decision-Making; 9.1 Background; 9.1.1 Levels of decision-making; 9.1.2 Modelled knowledge; 9.1.3 Methods; 9.2 Finite State Machines; 9.2.1 Computational FSM; 9.2.2 Mealy and Moore machines; 9.2.3 Implementation; 9.2.4 Discussion

9.3 Influence Maps9.4 Automated Planning; 9.5 Summary; Exercises; 10 Modelling Uncertainty; 10.1 Statistical Reasoning; 10.1.1 Bayes' theorem; 10.1.2 Bayesian networks; 10.1.3 Dempster-Shafer theory; 10.2 Fuzzy Sets; 10.2.1 Membership function; 10.2.2 Fuzzy operations; 10.2.3 Defuzzification; 10.3 Fuzzy Constraint Satisfaction Problem; 10.3.1 Modelling the criteria as fuzzy sets; 10.3.2 Weighting the importance of criteria; 10.3.3 Aggregating the criteria; 10.3.4 Making a decision; 10.4 Summary; Exercises; Part II Networking; 11 Communication Layers; 11.1 Physical Platform