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

摘要

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 schema- based approach 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 offline learning of schema chunks that could be saved and used later in an online search (using the regular CA algorithm) for coalition configurations. However, we do not want to sacrifice solution quality in making use of the evolutionary search technique. Thus, we compare the solutions discovered by the EL algorithm with those that are found using CA, as well as with a third algorithm that randomizes the CA algorithm, called RA. Four different applications in simulation are used to evaluate the EL, CA, and RA techniques. Our results show that the EL approach indeed finds solutions of comparable quality to the CA technique, while also providing the added benefit of learning highly fit partial solutions, or schema chunks, that may be beneficial for future tasks via constructivist learning. We conclude by arguing that the combination of the online CA search for solving current multi-robot tasks can be combined with the offline EL approach that can identify intermediate solutions (or schema chunks) that may be useful for future team tasks. This combination should lead to an overall efficiency improvement for identifying coalition formations, as well as for continual learning.