Case-based learning of applicability conditions for stochastic explanations

Abstract

This paper studies the problem of explaining events in stochastic environments. We explore three ideas to address this problem: (1) Using the notion of Stochastic Explanation, which associates with any event a probability distribution over possible plausible explanations for the event. (2) Retaining as cases (event, stochastic explanation) pairs when unprecedented events occur. (3) Learning the probability distribution in the stochastic explanation as cases are reused. We claim that a system using stochastic explanations reacts faster to abrupt changes in the environment than a system using deterministic explanations. We demonstrate this claim in a CBR system, incorporating the 3 ideas above, while playing a real-time strategy game. We observe how the CBR system when using stochastic explanations reacts faster to abrupt changes in the environment than when using deterministic explanations. © 2013 Springer-Verlag.