From Reactive to Cognitive Agents: Extending Reinforcement Learning to Generate Symbolic Knowledge Bases

Abstract

A new methodology for knowledge-based agents to learn from interactions with their environment is presented in this paper. This approach combines Reinforcement Learning and Knowledge-Based Systems. A Q-Learning algorithm obtains the optimal policy, which is automatically coded into a symbolic rule base, using first-order logic as knowledge representation formalism. The knowledge base was embedded in an omnidirectional mobile robot, making it able to navigate autonomously in unpredictable environments with obstacles using the same knowledge base. Additionally, a method of space abstraction based in human reasoning was formalized to reduce the number of complex environment states and to accelerate the learning. The experimental results of autonomous navigation executed by the real robot are also presented here.