Modeling the Creation and Development of Cause-Effect Pairs for Explanation Generation in a Cognitive Architecture.

The ability to generate explanations of perceived events and of one’s own actions is of central importance to how we make sense of the world. When modeling explanation generation, one common tactic used by cognitive systems is to construct a linkage of previously created causeeffect pairs. But where do such cause-effect pairs come from in the first place, and how can they be created automatically by cognitive systems? In this paper, we discuss the development of causal representations in children, by analyzing the literature surrounding a Piagetian experiment, and show how the conditions making cause-effect pair creation possible can start to be modeled using a combination of feature-extraction techniques and the structured knowledge representation in the hybrid cognitive architecture CLARION. We create a task in PAGI World for learning causality, and make this task available for download.