Plan Synthesis for Probabilistic Activity Recognition

Frank Krüger, Kristina Yordanova, Albert Hein, Thomas Kirste

Abstract

We analyze the applicability of model-based approaches to the task of inferring activities in smart environments. We introduce a symbolic approach to representing human behavior that enables the use of prior knowledge on the causality of human action and outline its probabilistic semantics. Based on an experimental analysis of a real world scenario from the smart meeting room domain, we show that such a symbolic approach allows to build reusable behavior models that compete with data-driven models at the performance level and that are able to track human behavior across a wide range of scenarios.

References

  1. Bonet, B. and Geffner, H. (2005). mGPT: A Probabilistic Planner Based on Heuristic Search. J. Artif. Intell. Res. (JAIR), 24:933-944.
  2. Hein, A., Burghardt, C., Giersich, M., and Kirste, T. (2009). Model-based Inference Techniques for detecting High-Level Team Intentions. In Gottfried, B. and Aghajan, H., editors, Behaviour Monitoring and Interpretation, volume 3. IOS Press.
  3. Hiatt, L. M., Harrison, A. M., and Trafton, J. G. (2011). Accommodating Human Variability in Human-Robot Teams through Theory of Mind. In Proc 22nd Intl J Conf on Artif. Intell. (IJCAI).
  4. Kaiser, K. and Miksch, S. (2004). Treating Temporal Information in Plan and Process Modeling. Technical Report Asgaard-TR-2004-1, Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna.
  5. Kirste, T. and Krü ger, F. (2012). CCBM-A tool for activity recognition using Computational Causal Behavior Models. Technical Report CS-01-12, Institut für Informatik, Universität Rostock, Rostock, Germany. ISSN 0944-5900.
  6. Okeyo, G., Chen, L., Wang, H., and Sterritt, R. (2011). Ontology-Based Learning Framework for Activity Assistance in an Adaptive Smart Home. In Chen, L. et al., editors, Activity Recognition in Pervasive Intelligent Environments, volume 4. Atlantis Press.
  7. Ramirez, M. and Geffner, H. (2011). Goal Recognition over POMDPs: Inferring the Intention of a POMDP Agent. In Proc 22nd Intl J Conf on Artif. Intell. (IJCAI).
  8. Roy, P. C. et al. (2011). A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer's Patients. In Chen, L. et al., editors, Activity Recognition in Pervasive Intelligent Environments, volume 4. Atlantis Press.
  9. Sadilek, A. and Kautz, H. (2012). Location-Based Reasoning about Complex Multi-Agent Behavior. J. Artif. Intell. Res. (JAIR), 43:87-133.
  10. Storf, H., Becker, M., and Riedl, M. (2009). Rule-based activity recognition framework: Challenges, technique and learning. In Proc. 3rd Intl. Conf. on Pervasive Computing Technologies for Healthcare.
Download


Paper Citation


in Harvard Style

Krüger F., Yordanova K., Hein A. and Kirste T. (2013). Plan Synthesis for Probabilistic Activity Recognition . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 283-288. DOI: 10.5220/0004256002830288


in Bibtex Style

@conference{icaart13,
author={Frank Krüger and Kristina Yordanova and Albert Hein and Thomas Kirste},
title={Plan Synthesis for Probabilistic Activity Recognition},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={283-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004256002830288},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Plan Synthesis for Probabilistic Activity Recognition
SN - 978-989-8565-39-6
AU - Krüger F.
AU - Yordanova K.
AU - Hein A.
AU - Kirste T.
PY - 2013
SP - 283
EP - 288
DO - 10.5220/0004256002830288