Inducing Hierarchical Process Models in Dynamic Domains


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.

Proceedings of the Twentieth National Conference on Artificial Intelligence