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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Denzinger, J. Kordt, M. |
| Copyright Year | 2000 |
| Description | Author affiliation: Fachbereich Inf., Kaiserslautern Univ., Germany (Denzinger, J.) |
| Abstract | We present a concept to use off-line learning approaches to achieve online learning of cooperative behavior of agents and instantiate this concept for evolutionary learning with agents based on prototype situation-action-pairs and the nearest-neighbor rule. For such an agent model also modeling of other agents can be achieved using the agent's own architecture with situation-action-pairs derived from observations. We tested our online learning agents for different variants of the pursuit game and characterize the aspects of variants for which our online learning agents outperform off-line learning ones. Since our concept also allows a smooth transition from off-line learning to online learning and vice versa, the resulting system is able to win much more game variants than systems using either on- or off-line learning exclusively. |
| Starting Page | 103 |
| Ending Page | 110 |
| File Size | 240780 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769506259 |
| DOI | 10.1109/ICMAS.2000.858441 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-07-12 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning systems Multiagent systems Neural networks Decision making Prototypes Testing Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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