Inducing Hierarchical Process Models in Dynamic Domains

Ljupco Todorovski, Will Bridewell, Oren Shiran, Pat Langley

Research on inductive process modeling combines background knowledge with time-series data to construct explanatory 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 hierarchical 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 accurate and plausible models with less effort. We conclude by discussing related research and directions for future work.

Content Area: 12. Machine Learning

Subjects: 12.2 Scientific Discovery

Submitted: May 10, 2005


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