Motivated learning in autonomous systems

Motivated learning (ML) is a new biologically inspired machine learning method. It is the combination of a reinforcement learning (RL) algorithm and a system that creates hierarchy of goals. The goal creation system is concerned with creating new internal goals, building a hierarchy of them, and controlling the agent's behavior according to this constituted hierarchy of goals. As in case of reinforcement learning method, a motivated learning agent is learning through interaction with the environment. The comparisons of both methods in special type test environment show that the motivated learning method is more efficient in learning complex relations between available resources (concepts). ML has better performance than RL, especially in dynamically changing environments. In the presented experiments we have shown that ML based agent, which has the ability to set its internal goals autonomously, is able to fulfill the designer's goals more effectively than RL based agent. In addition, because the observed concepts are not predefined but emerge during the learning process, this method also addresses problem of merging connectionist and symbolic approaches for intelligent autonomous systems.