Towards schema-based, constructivist robot learning: Validating an evolutionary search algorithm for schema chunking

— In this paper, we lay the groundwork for extending our previously developed ASyMTRe architecture to enable constructivist learning for multi-robot team tasks. The ASyMTRe architecture automatically configures schemas within, and across, robots to form the highest utility solution that achieves a given multi-robot team task. We believe that the schemaproach used in ASyMTRe is a useful abstraction not only for forming heterogeneous coalitions, but also for enabling constructivist learning, in which chunks of schemas that solve intermediate subproblems are learned and then made available for future task solutions. However, the existing ASyMTRe search algorithm for finding configurations of schemas that completely solve given tasks (Centralized ASyMTRe – CA) is not well-suited for identifying useful chunks of schemas that could solve intermediate subtasks that may be useful in the future. Thus, in this current work, we explore an Evolutionary Learning (EL) technique for the offli...

• Type: Conference
• Year: 2008
• Where: ICRA
• Authors: Yifan Tang, Lynne E. Parker
• Keywords: CA Algorithm, Constructivist Learning, ICRA 2008, Multi-robot Team Task, Robotics