Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Technical Note: \cal Q-Learning
Machine Learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Artificial life meets entertainment: lifelike autonomous agents
Communications of the ACM
Machine Learning
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
Creatures: artificial life autonomous software agents for home entertainment
AGENTS '97 Proceedings of the first international conference on Autonomous agents
The theory of evolution strategies
The theory of evolution strategies
Games, computers and artificial intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Machine learning in games: a survey
Machines that learn to play games
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Ai Game Engine Programming (Game Development Series)
Ai Game Engine Programming (Game Development Series)
Automatic feature selection in neuroevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Adaptive game AI with dynamic scripting
Machine Learning
Learning tetris using the noisy cross-entropy method
Neural Computation
Evolving controllers for simulated car racing using object oriented genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Adaptive mixtures of local experts
Neural Computation
Acquiring visibly intelligent behavior with example-guided neuroevolution
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man
Journal of Artificial Intelligence Research
Reinforcement learning in distributed domains: beyond team games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Evolutionary computation and games
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Playing to learn: case-injected genetic algorithms for learning to play computer games
IEEE Transactions on Evolutionary Computation
Real-time rule-based classification of player types in computer games
User Modeling and User-Adapted Interaction
Hi-index | 0.00 |
Artificial intelligence for digital games constitutes the implementation of a set of algorithms and techniques from both traditional and modern artificial intelligence in order to provide solutions to a range of game dependent problems. However, the majority of current approaches lead to predefined, static and predictable game agent responses, with no ability to adjust during game-play to the behaviour or playing style of the player. Machine learning techniques provide a way to improve the behavioural dynamics of computer controlled game agents by facilitating the automated generation and selection of behaviours, thus enhancing the capabilities of digital game artificial intelligence and providing the opportunity to create more engaging and entertaining game-play experiences. This paper provides a survey of the current state of academic machine learning research for digital game environments, with respect to the use of techniques from neural networks, evolutionary computation and reinforcement learning for game agent control.