From Global Selective Perception to Local Selective Perception

This paper presents a reinforcement learning algorithm used to allocate tasks to agents in an uncertain real-time environment. In such environment, tasks have to be analyzed and allocated really fast for the multiagent system to be effective. To analyze those tasks, described by a lot of attributes, we have used a selective perception technique to enable agents to narrow down the description of each task, enabling the reinforcement learning algorithm to work on a problem with a reasonable number of possible states.

• Type: Conference
• Year: 2004
• Where: ATAL
• Authors: Sébastien Paquet, Nicolas Bernier, Brahim Chaib-draa
• Keywords: ATAL 2004, Reinforcement Learning Algorithm, Selective Perception Technique, Uncertain Real-time Environment