Efficient compositional modeling for generating causal explanations

摘要

Effective problem solving requires building adequate models that embody the simplifications, abstractions, and approximations that parsimoniously describe the relevant system phenomena for the task at hand. Compositional modeling is a framework for constructing adequate device models by composing model fragments selected from a model fragment library. While model selection using compositional modeling has been shown to be intractable, it is tractable when all model fragment approximations are causal approximations.