Automated Model Selection for Simulation

Yumi Iwasaki, Alon Y. Levy

Constructing an appropriate model is crucial in reasoning successfully about the behavior of a physical situation to answer a query. In compositional modeling, a system is provided with a library of composible pieces of knowledge about the physical world called model fragments. Its task is to select appropriate model fragments to describe the situation, either for static analysis of a single state, or for the more complicated case simulation of dynamic behavior over a sequence of states. In previous work we showed how the model construction problem in general can advantageously be formulated as a problem of reasoning about relevance. This paper presents an actual algorithm, based on relevance reasoning, for selecting model fragments efficiently for the case of simulation. We show that the algorithm produces an adequate model for a given query and moreover, it is the simplest one given the constraints in the query.

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