Back to Search
Start Over
FAST AND ROBUST INCREMENTAL ACTION PREDICTION FOR INTERACTIVE AGENTS.
- Source :
-
Computational Intelligence . Feb2005, Vol. 21 Issue 1, p90-110. 21p. 3 Black and White Photographs, 4 Diagrams, 3 Charts, 4 Graphs. - Publication Year :
- 2005
-
Abstract
- The ability for a given agent to adapt on-line to better interact with another agent is a difficult and important problem. This problem becomes even more difficult when the agent to interact with is a human, because humans learn quickly and behave nondeterministically. In this paper, we present a novel method whereby an agent can incrementally learn to predict the actions of another agent (even a human), and thereby can learn to better interact with that agent. We take a case-based approach, where the behavior of the other agent is learned in the form of state–action pairs. We generalize these cases either through continuousk-nearest neighbor, or a modified bounded minimax search. Through our case studies, our technique is empirically shown to require little storage, learn very quickly, and be fast and robust in practice. It can accurately predict actions several steps into the future. Our case studies include interactive virtual environments involving mixtures of synthetic agents and humans, with cooperative and/or competitive relationships. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08247935
- Volume :
- 21
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Computational Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 16018444
- Full Text :
- https://doi.org/10.1111/j.0824-7935.2005.00266.x