A Learning Classifier System for Emergent Team Behavior in Real-Time POMDP
Often the only solution for many complex and dynamic real-world situations is a crucial concurrent cooperation and coordination divided into tasks and subtasks, i.e. team behavior 1. This research focus on such problems under real-time constraints, distributed control and decentralized knowledge. Existent frameworks and simulation systems were designed relying heavily on a priori knowledge of experts and introducing little or nothing of Machine Learning (ML). Therefore, the goal here is to develop a team of agents inspired by team behavior as found in Nature — emergent and adaptive — applying only ML on the action-selection decision process. Such team would reduce time and resources in the design of autonomous teamwork while keeping equivalent performance in comparison to a heuristic-based approach. Applying unbiased methods and a divide and conquer strategy, we achieved individual actions that emerge into the aimed collective behavior, not once requiring plans, common beliefs or agreed intentins.
Team Behavior POMDP Learning Classifier System Genetic Algorithm Multi Agent Systems.
Isabela Anciutti
Knowledge-based Systems University of Paderborn 33098 Paderborn,Germany
国际会议
上海
英文
733-738
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）