A learning infrastructure for improving agent performance and game balance

AAAI Workshop - Technical Report

Volumn WS-07-01, Issue , 2007, Pages 7-12

  Ludwig, Jeremy ^a   Farley, Art ^a  

^a UNIVERSITY OF OREGON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGENT PERFORMANCE; DIGITAL ENTERTAINMENT; DYNAMIC SCRIPTING; MODERN COMPUTER GAMES; REAL TIME STRATEGIES; REINFORCEMENT LEARNING ALGORITHMS;

AGENTS; ARTIFICIAL INTELLIGENCE; EDUCATION; GAME THEORY; LEARNING SYSTEMS; OPTIMIZATION; REINFORCEMENT; REINFORCEMENT LEARNING;

LEARNING ALGORITHMS;

EID: 51849146499     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Aha, D. W., Molineaux, M., & Ponsen, M. (2005). Learning to win: Case-based plan selection in a real-time strategy game. Paper presented at the Sixth International Conference on Case-Based Reasoning, Chicago, IL.
2. Andrade, G., Ramalho, G., Santana, H., & Comible, V. (2005). Extending reinforcement learning to provide dynamic game balancing. Paper presented at the Reasoning, Representation, and Learning in Computer Games: Proceedings of the IJCAI workshop, Edinburgh, Scotland.
3. Au, M., & Maire, F. (2004). Automatic State Construction using Decision Tree for Reinforcement Learning Agents. Paper presented at the International Conference on Computational Intelligence for Modelling, Control and Automation, Gold Coast, Australia.
4. Dahlbom, A., & Niklasson, L. (2006). Goal-directed hierarchical dynamic scripting for RTS games. Paper presented at the Second Artificial Intelligence in Interactive Digital Entertainment, Marina del Rey, California.
5. Fu, D., Houlette, R., & Ludwig, J. (2007). An AIModeling Tool for Designers and Developers. Paper presented at the IEEE Aerospace Conference.
6. Laird, J. E., & van Lent, M. (2001). Human-level AI's killer application: Interactive computer games. AI Magazine, 22(2), 15-26.
7. Marthi, B., Russell, S., & Latham, D. (2005). Writing stratagus-playing agents in concurrent ALisp. Paper presented at the Reasoning, representation, and learning in computer games: Proceedings of the IJCAI workshop, Edinburgh, Scotland.
8. Ponsen, M., Spronck, P., & Tuyls, K. (2006). Hierarchical reinforcement leanring in computer games. Paper presented at the ALAMAS'06 Adaptive Learning and Multi-Agent Systems, Vrije Universiteit, Brussels, Belgium.
9. Schunn, C. D. (2001). Goodness of Fit Metrics in Comparing Models to Data. Retrieved 06/06, 2004, from http://www.lrdc.pitt.edu/schunn/gof/index.btm]
10. Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., & Postma, E. (2006). Adaptive game AI with dynamic scripting. Machine Learning, 55(3), 217-248.
11. Sutton, R., & Barto, A. (1998). Reinforcement Learning: An Introduction: MIT Press.
12. Witten, I. H., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques, 2nd Edition. San Francisco: Morgan Kaufmann.
13. Yannakakis, G. N., & Hallam, J. (2005). A scheme for creating digital entertainment with substance. Paper presented at the Reasoning, representation, and learning in computer games: Proceedings of the IJCAI workshop, Edinburgh, Scotland.