Efficient compositional modeling for generating causal explanations

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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. This paper addresses the reasoning and knowledge representation issues that arise in building practical systems for constructing adequate device models that provide parsimonious causal explanations of how a device functions. We make four important contributions. First, we present a representation of class level descriptions of model fragments and their relationships. The representation yields a practical model fragment library organization that facilitates knowledge base construction and supports focused generation of device models. Second, we show how the structural, behavioral, and functional contexts of the device define model adequacy and provide the task focus and additional constraints to guide the search for adequate models. Third, we describe a novel model selection algorithm that incorporates device behavior with order of magnitude reasoning and focuses model selection with component interaction heuristics. Fourth, we present the results of our implementation that produces adequate models and causal explanations of a variety of electromechanical devices drawn from a library of 20 components and 150 model fragments.




Nayak, P. P., & Joskowicz, L. (1996). Efficient compositional modeling for generating causal explanations. Artificial Intelligence, 83(2), 193–227. https://doi.org/10.1016/0004-3702(95)00024-0

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