Inducing hierarchical process models in dynamic domains

Abstract

Research on inductive process modeling combines back-ground knowledge with time-series data to construct explana-tory models, but previous work has placed few constraints on search through the model space. We present an extended formalism that organizes process knowledge in a hierarchi-cal manner, and we describe HIPM, a system that carries out constrained search for hierarchical process models. We report experiments that suggest this approach produces more accu-rate and plausible models with less effort. We conclude by discussing related research and directions for future work.

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