Plan Recognition: Probabilistic and Hybrid Methods

Single Agent Scenarios

• E. Charniak and R. Goldman. (1993). A Bayesian Model of Plan Recognition. Artificial Intelligence, 64: 53-79.

• J. Pearl and J. M. Robins. (1995). Probabilistic evaluation of sequential plans from causal models with hidden variables. In P. Besnard and S. Hanks (Eds.) Uncertainty in Artificial Intelligence, vol. II, pp.444-53. San Francisco: Morgan Kaufmann.

• R. Goldman, C. Geib, C. Miller. (1999). A New Model of Plan Recognition. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 245-254.

• Wenji Mao, Jonathan Gratch. (2004) Decision-Theoretic Approach to Plan Recognition. ICT Technical Report ICT-TR-01-2004.

• Dorit Avrahami-Zilberbrand and Gal A. Kaminka (2006). Hybrid Symbolic-Probabilistic Plan Recognizer: Initial steps. In Proceedings of the AAAI Workshop on Modeling Others from Observations (MOO-06).

• David V. Pynadath and Michael P. Wellman (2000). Probabilistic State-dependent Grammars for Plan Recognition. In Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI), pp. 507-514.

• Einat Marhasev, Meirav Hadad, and Gal A. Kaminka (2006). Non-stationary Hidden Semi Markov Models in Activity Recognition. In Proceedings of the AAAI Workshop on Modeling Others from Observations (MOO-06), 2006.

• Nate Blaylock, and James Allen (2006). Fast Hierarchical Goal Schema Recognition, Proceedings of AAAI 2006.

• Hung H. Bui, Svetha Venkatesh, Geoff West (2002). Policy Recognition in the Abstract Hidden Markov Model, JAIR, vol. 17, pp. 451-499

Multi-Agent Scenarios

• Gal A. Kaminka and David V. Pynadath and Milind Tambe (2002). Monitoring Teams by Overhearing: A Multi-Agent Plan Recognition Approach, JAIR, 17, 2002.

• S Saria, S Mahadevan (2004). Probabilistic Plan Recognition in Multiagent Systems, ICAPS-2004.

Plan Recognition: Symbolic Methods

Single Agent scenarios

• D. Avrahami-Zilberbrand and G. Kaminka (2005). Fast and complete symbolic plan recognition, IJCAI-2005.

• D. Avrahami-Zilberbrand and G. Kaminka AND Hila Zarosim (2005). Fast and complete symbolic plan recognition" Allowing for Duration, Interleaved Execution, and Lossy Observations, MOO-2005.

• Jarvis, P., Lunt, T., and Myers, K. (2004). Identifying Terrorist Activity with AI Plan Recognition Technology, IAAI-2004.

Multi-Agent scenarios:

• Mark Devaney, Ashwin Ram (1998). Needles in a Haystack: Plan Recognition in Large Spatial Domains Involving Multiple Agents, AAAI-1998.

Deception Detection

• Eugene Santos Jr., Qunhua Zhao, Gregory Johnson, Jr., Hien Nguyen and Paul Thompson (2005). A Cognitive Framework for Information Gathering with Deception Detection for Intelligence Analysis, IA-2005.

• Frank J. Stech and Christopher Elsaesser. (2005). Deception Detection by Analysis of Competing Hypotheses, IA-2005.

• P. E. Johnson and S. Grazioli, K. Jamal (1993). Fraud detection: Intentionality and deception in cognition, Accounting, Organizations and Society, Volume 18, pp 467-488.

• Gregory Johnson Jr and Eugene Santos Jr (2004). Detecting Deception in Intelligent Systems I: Activation of Deception Detection Tactics, LNCS, vol. 3060/2004, pp.339-354

Hierarchical HMM-s

• Shai Fine, Yoram Singer, Naftali Tishby (1998). The Hierarchical Hidden Markov Model: Analysis and Applications, Machine Learning, vol. 32, pp 41-62.

General Background on HMM-s, Dynamic Bayes Nets

• Kevin P. Murphy. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Thesis. UC Berkeley, Computer Science Division, July 2002.

          "... [a] catholic presentation of the field of sequential data modelling."

HTN: Hierarchical Task Networks

• Kutluhan Erol, James Hendler, Dana S. Nau (1994). Semantics for Hierarchical Task-Network Planning Technical Report CS-TR-3239, UMIACS-TR-94-31, ISR-TR-95-9, March 1994.

• Kutluhan Erol, James Hendler, Dana S. Nau (1995). Complexity Results for HTN Planning. Annals of Mathematics and Artificial Intelligence

• Andrew Garland and Neal Lesh (2003). Learning Hierarchical Task Models By Decomposition. Technical Report: TR2003?-01, January 2003, Mitsubishi Electric Research Laboratories [http://www.merl.com]

Logic, Relations and Probability

• Kathryn Blackmond Laskey. (2006). MEBN: A Logic for Open-World Probabilistic Reasoning. George Mason University C4I? Center Technical Report C4I06?-01, February, 2006.

• M. Richardson and P. Domingos. Markov Logic Networks. Machine Learning. To appear.

• P. Carbonetto, J. Kisynski, N. de Freitas and D. Poole. (2005). Nonparametric Bayesian Logic. Proceedings of UAI 2005.

• D. Poole. (2003). First Order Probabilistic Inference IJCAI 2003.