This paper deals with cooperation for virtual reality applications. In a multi-agent system, cooperation between agents is an important element to solve a common task, which is very difficult or impossible for a single agent or a group of agents without cooperation. Hence we focus on cooperation in the predator-prey problem where a group of programmed and learning predators coordinates their actions to capture the prey. These actions of a learning predator are dynamically weighted by a behavioral system based on motor schemas and classifier systems. At each instant, the system must modify the weights in order to enhance the strategies of the group, as surrounding a prey. Thanks to the classifier system the learning predator learns situations and gradually adapts its actions to its environment. First encouraging results show that coupling such systems gives very efficient performances in dynamic environments.
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- Learning Cooperation from Classifier Systems
Trung Hau Tran
- Springer Berlin Heidelberg