Knowledge-based Extraction of Area of Expertise for Cooperation in Learning

Using each other's knowledge and expertise in learning - what we call cooperation in learning- is one of the major existing methods to reduce the number of learning trials, which is quite crucial for real world applications. In situated systems, robots become expert in different areas due to being exposed to different situations and tasks. As a consequence, areas of expertise (AOE) of the other agents must be detected before using their knowledge, especially when the exchanged knowledge is not abstract, and simple information exchange might result in incorrect knowledge, which is the case for Q-learning agents. In this paper we introduce an approach for extraction of AOE of agents for cooperation in learning using their Q-tables. The evaluating robot uses a behavioral measure to evaluate itself, in order to find a set of states it is expert in. That set is used, then, along with a Q-table-based feature for extraction of areas of expertise of other robots by means of a classifier. Extracted areas are merged in the last stage. The proposed method is tested both in extensive simulations and in real world experiments using mobile robots. The results show effectiveness of the introduced approach, both in accurate extraction of areas of expertise and increasing the quality of the combined knowledge, even when, there are uncertainty and perceptual aliasing in the application and the robot