Incremental behavior acquisition based on reliability of observed behavior recognition

We propose a novel approach for acquisition and development of behaviors through observation in multi-agent environment. Observed behaviors of others give fruitful hints for a learner to find a new situation, a new behavior for the situation, necessary information for the behavior acquisition. RoboCup scenario gives us a good test-bed multi-agent environment where a learner can observe behaviors of others during practices or games. It is more realistic, practical, and efficient to take advantages of observation of skilled players than to discover new skills and necessary information only through the interaction of a learner and an environment. The learner automatically detects state variables and a goal of the behavior through the observation based on mutual information. Reinforcement learning method is applied to acquire the discovered behavior suited to the robot. Experiments under RoboCup MSL scenario shows the validity of the proposed method.