Causal Learning versus Reinforcement Learning for Knowledge Learning and Problem Solving

Causal learning and reinforcement learning are both important AI learning mechanisms but are usually treated separately, despite the fact that both are directly relevant to problem solving processes. In this paper we propose a method for causal learning and problem solving, and compare and contrast that with AI reinforcement learning and show that the two methods are actually related, differing only in the values of the learning rate α and discount factor γ. However, the causal learning framework emphasizes quick but non-optimal concoction of problem solutions while AI reinforcement learning generates optimal solutions at the expense of speed. Cognitive science literature is reviewed and it is found that psychological reinforcement learning in lower form animals such as mammals is distinct from AI reinforcement learning in that psychological reinforcement learning strives neither for speed nor optimality, and that higher form animals such as humans and primates employ quick causal learning for survival instead of reinforcement learning. AI systems should likewise take advantage of a framework that employs rapid inductive causal learning to generate problem solutions for its general viability in terms of rapid adaptability, without the need to always strive for optimality.