Agents-based modeling for a peer-to-peer MMOG architecture
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Evolving Game NPCs Based on Concurrent Evolutionary Neural Networks
Edutainment '08 Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment
Evolving team behaviours in environments of varying difficulty
Artificial Intelligence Review
Learning Human-Level AI abilities to drive racing cars
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Immune clonal selection algorithm for hybrid flow-shop scheduling problem
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Mobile robots navigation based on graph search techniques
ECCE'10/ECCIE'10/ECME'10/ECC'10 Proceedings of the European conference of chemical engineering, and European conference of civil engineering, and European conference of mechanical engineering, and European conference on Control
Decision tree-based algorithms for implementing bot AI in UT2004
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Real-Time neuroevolution to imitate a game player
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
Depictions of genotypic space for evaluating the suitability of different recombination operators
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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From the Publisher:AI Techniques for Game Programming takes the difficult topics of genetic algorithms and neural networks and explains them in plain English. Gone are the tortuous mathematic equations and abstract examples to be found in other books. Each chapter takes you through the theory a step at a time, explaining clearly how you can incorporate each technique into your own games. After a whirlwind tour of Windows programming, you will learn how to use genetic algorithms for optimization, path-finding, and evolving control sequences for your game agents. Coverage of neural network basics quickly advances to evolving neural motion controllers for your game agents and applying neural networks to obstacle avoidance and map exploration. Backpropagation and pattern recognition is also explained. By the time you聮re done, you聮ll know how to train a network to recognize mouse gestures and how to use state-of-the-art techniques for creating neural networks with dynamic topologies.