Enhancing Inference in Relational Reinforcement Learning Via Truth Maintenance Systems

Computational complexity is still a challenging problem for intelligent systems operating in compound environments. To tackle it, an agent has to deal with perceptual information intelligently. In this paper, we propose an efficient and adaptive reasoning system based on Adaptive Logic Interpreter reasoning system, a mechanism for guiding inference through relational reinforcement learning, and a variation of Truth Maintenance Systems to speed up the inference. Relational reinforcement learning guides the inference toward the most rewarding parts of the knowledge base and truth maintenance system maintains beliefs, avoids repetitive inferences and reduces the state space. Empirical results demonstrate higher performance than the basic approach in terms of number of inferred instances, average reward, and average reward accuracy.