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Mode Estimation of Model-Based Programs: Monitoring Systems with Complex Behavior

Brian C. Williams, Seung Chung, Vineet Gupta
IJCAI 2001 pp. 579-590
doi:10.13140/2.1.5135.9680 /ijcai/2001/williams2001ijcai-mode/

Abstract

Deductive mode-estimation has become an essential component of robotic space systems, like NASA’s deep space probes. Future robots will serve as components of large robotic networks. Monitoring these networks will require modeling languages and estimators that handle the sophisticated behaviors of robotic components. This paper introduces RMPL, a rich modeling language that combines reactive programming constructs with probabilistic, constraint-based modeling, and that offers a simple semantics in terms of hidden Markov models (HMMs). To support efficient realtime deduction, we translate RMPL models into a compact encoding of HMMs called probabilistic hierarchical constraint automata (PHCA). Finally, we use these models to track a system’s most likely states by extending traditional HMM belief update. 1

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Text

Williams et al. "Mode Estimation of Model-Based Programs: Monitoring Systems with Complex Behavior." International Joint Conference on Artificial Intelligence, 2001. doi:10.13140/2.1.5135.9680

Markdown

[Williams et al. "Mode Estimation of Model-Based Programs: Monitoring Systems with Complex Behavior." International Joint Conference on Artificial Intelligence, 2001.](https://mlanthology.org/ijcai/2001/williams2001ijcai-mode/) doi:10.13140/2.1.5135.9680

BibTeX

@inproceedings{williams2001ijcai-mode,
  title     = {{Mode Estimation of Model-Based Programs: Monitoring Systems with Complex Behavior}},
  author    = {Williams, Brian C. and Chung, Seung and Gupta, Vineet},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2001},
  pages     = {579-590},
  doi       = {10.13140/2.1.5135.9680},
  url       = {https://mlanthology.org/ijcai/2001/williams2001ijcai-mode/}
}