A Hybrid Cognitive-Reactive Multi-Agent Controller

The purpose of this paper is to introduce a hybrid cognitive-reactive system, which integrates a machine- learning algorithm (SAMUEL, an evolutionary algorithm-based rule-learning system) with a computational cognitive model (written in ACT-R). In this system, the learning algorithm handles reactive aspects of the task and provides an adaptation mechanism, while the cognitive model handles cognitive aspects of the task and ensures the realism of the behavior. In this study, the controller architecture is used to implement a controller for a team of micro-air vehicles performing reconnaissance and surveillance.
Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2002, Lausanne, Switzerland on 30 Sep-4 Oct 2002 and published in proceedings of the same. Sponsored in part by DARPA.