Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
How to solve it: modern heuristics
How to solve it: modern heuristics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
AI and the Entertainment Industry
IEEE Intelligent Systems
Object-Oriented Game Development
Object-Oriented Game Development
Automatically acquiring domain knowledge for adaptive game AI using evolutionary learning
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Knowledge acquisition for adaptive game AI
Science of Computer Programming
Evolving explicit opponent models in game playing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Imitation Learning in Uncertain Environments
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Machine learning in digital games: a survey
Artificial Intelligence Review
Extending the Strada Framework to Design an AI for ORTS
ICEC '09 Proceedings of the 8th International Conference on Entertainment Computing
Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man
Journal of Artificial Intelligence Research
Controller for TORCS created by imitation
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Computationally efficient behaviour based controller for real time car racing simulation
Expert Systems with Applications: An International Journal
Using data mining for dynamic level design in games
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Real-time team-mate AI in games: a definition, survey, & critique
Proceedings of the Fifth International Conference on the Foundations of Digital Games
Neural networks training for weapon selection in first-person shooter games
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
cOncienS: Organizational Awareness in Real-Time Strategy Games
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Guiding user adaptation in serious games
Agents for games and simulations II
Making games ALIVE: an organisational approach
Agents for games and simulations II
International Journal of Computer Games Technology
A semantic generation framework for enabling adaptive game worlds
Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology
Organizing scalable adaptation in serious games
AEGS'11 Proceedings of the 2011 international conference on Agents for Educational Games and Simulations
Formalizing the construction of populations in multi-agent simulations
Engineering Applications of Artificial Intelligence
Using gameplay semantics to procedurally generate player-matching game worlds
Proceedings of the The third workshop on Procedural Content Generation in Games
Hi-index | 0.00 |
Online learning in commercial computer games allows computer-controlled opponents to adapt to the way the game is being played. As such it provides a mechanism to deal with weaknesses in the game AI, and to respond to changes in human player tactics. We argue that online learning of game AI should meet four computational and four functional requirements. The computational requirements are speed, effectiveness, robustness and efficiency. The functional requirements are clarity, variety, consistency and scalability. This paper investigates a novel online learning technique for game AI called `dynamic scripting', that uses an adaptive rulebase for the generation of game AI on the fly. The performance of dynamic scripting is evaluated in experiments in which adaptive agents are pitted against a collection of manually-designed tactics in a simulated computer roleplaying game. Experimental results indicate that dynamic scripting succeeds in endowing computer-controlled opponents with adaptive performance. To further improve the dynamic-scripting technique, an enhancement is investigated that allows scaling of the difficulty level of the game AI to the human player's skill level. With the enhancement, dynamic scripting meets all computational and functional requirements. The applicability of dynamic scripting in state-of-the-art commercial games is demonstrated by implementing the technique in the game Neverwinter Nights. We conclude that dynamic scripting can be successfully applied to the online adaptation of game AI in commercial computer games.