ML Anthology

Inducing Hierarchical Process Models in Dynamic Domains

Ljupco Todorovski, Will Bridewell, Oren Shiran, Pat Langley
AAAI 2005 pp. 892-897
/aaai/2005/todorovski2005aaai-inducing/

Abstract

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.

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Text

Todorovski et al. "Inducing Hierarchical Process Models in Dynamic Domains." AAAI Conference on Artificial Intelligence, 2005.

Markdown

[Todorovski et al. "Inducing Hierarchical Process Models in Dynamic Domains." AAAI Conference on Artificial Intelligence, 2005.](https://mlanthology.org/aaai/2005/todorovski2005aaai-inducing/)

BibTeX

@inproceedings{todorovski2005aaai-inducing,
  title     = {{Inducing Hierarchical Process Models in Dynamic Domains}},
  author    = {Todorovski, Ljupco and Bridewell, Will and Shiran, Oren and Langley, Pat},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {892-897},
  url       = {https://mlanthology.org/aaai/2005/todorovski2005aaai-inducing/}
}