Applying Ant Colony Optimization algorithms for high-level behavior learning and reproduction from demonstrations

In domains where robots carry out human's tasks, the ability to learn new behaviors easily and quickly plays an important role. Two major challenges with Learning from Demonstration (LfD) are to identify what information in a demonstrated behavior requires attention by the robot, and to generalize the learned behavior such that the robot is able to perform the same behavior in novel situations.The main goal of this paper is to incorporate Ant Colony Optimization (ACO) algorithms into LfD in an approach that focuses on understanding tutor's intentions and learning conditions to exhibit a behavior. The proposed method combines ACO algorithms with semantic networks and spreading activation mechanism to reason and generalize the knowledge obtained through demonstrations. The approach also provides structures for behavior reproduction under new circumstances. Finally, applicability of the system in an object shape classification scenario is evaluated. A behavior learning method based on Ant Colony Optimization (ACO) is proposed.We combined ACO, Semantic Networks and Spreading Activation mechanisms.The method is able to learn high-level aspects of behaviors from demonstrations.The method answers questions of "What to imitate" and "When to imitate".The method generalizes concepts while learning high-level aspects of behaviors.