Hierarchical Goal Recognition

This chapter discusses hierarchical goal recognition: simultaneous online recognition of goals and subgoals at various levels within an HTN-like plan tree. We use statistical, graphical models to recognize hierarchical goal schemas in time quadratic with the number of the possible goals. Within our formalism, we treat goals as parameterized actions, necessitating the recognition of parameter values as well. The goal schema recognizer is combined with a tractable version of the Dempster-Shafer theory to predict parameter values for each goal schema. This results in a tractable goal recognizer that can be trained on any plan corpus (a set of hierarchical plan trees). Additionally, we comment on the state of data availability for plan recognition in general and briefly describe a system for generating synthetic data using a mixture of AI planning and Monte Carlo sampling. This was used to generate the Monroe Corpus, one of the first large plan corpora used for training and evaluating plan recognizers. This chapter also discusses the need for general metrics for evaluating plan recognition and proposes a set of common metrics.

[1]  Nate Blaylock,et al.  Generating Artificial Corpora for Plan Recognition , 2005, User Modeling.

[2]  Svetha Venkatesh,et al.  Policy Recognition in the Abstract Hidden Markov Model , 2002, J. Artif. Intell. Res..

[3]  Ingrid Zukerman,et al.  Bayesian Models for Keyhole Plan Recognition in an Adventure Game , 2004, User Modeling and User-Adapted Interaction.

[4]  Hung H. Bui,et al.  Efficient Approximate Inference for Online Probabilistic Plan Recognition , 2002 .

[5]  Dana S. Nau,et al.  SHOP2: An HTN Planning System , 2003, J. Artif. Intell. Res..

[6]  Oren Etzioni,et al.  Scalable and adaptive goal recognition , 1998 .

[7]  Mathias Bauer.  A dempster-shafer approach to modeling agent preferences for plan recognition , 2005, User Modeling and User-Adapted Interaction.

[8]  Robert P. Goldman,et al.  Recognizing Plan/Goal Abandonment , 2003, IJCAI.

[9]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[10]  Nate Blaylock,et al.  Statistical Goal Parameter Recognition , 2004, ICAPS.

[11]  Henry A. Kautz,et al.  Location-Based Reasoning about Complex Multi-Agent Behavior , 2012, J. Artif. Intell. Res..

[12]  Alex Pentland,et al.  Graphical Models for Recognizing Human Interactions , 1998, NIPS.

[13]  Mathias Bauer.  Acquisition of User Preferences for Plan Recognition , 2007 .

[14]  Eric Horvitz,et al.  A computational architecture for conversation , 1999 .

[15]  Sandra Carberry,et al.  Techniques for Plan Recognition , 2001, User Modeling and User-Adapted Interaction.

[16]  Mathias Bauer,et al.  Logic-based Plan Recognition for Intelligent Help Systems , 1994, PuK.

[17]  Mel Siegel,et al.  Sensor data fusion for context-aware computing using dempster-shafer theory , 2004 .

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[19]  Kevin P. Murphy,et al.  Linear-time inference in Hierarchical HMMs , 2001, NIPS.

[20]  Mathias Bauer,et al.  Acquisition of Abstract Plan Descriptions for Plan Recognition , 1998, AAAI/IAAI.

[21]  Brian D. Davison,et al.  Predicting Sequences of User Actions , 1998 .

[22]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

[23]  Hung Hai Bui,et al.  A General Model for Online Probabilistic Plan Recognition , 2003, IJCAI.

[24]  Michael P. Wellman,et al.  Probabilistic State-Dependent Grammars for Plan Recognition , 2000, UAI.

[25]  Eugene Charniak,et al.  Statistical Techniques for Natural Language Parsing , 1997, AI Mag..

[26]  Tom M. Mitchell,et al.  Extracting Knowledge about Users' Activities from Raw Workstation Contents , 2006, AAAI.

[27]  Candace L. Sidner,et al.  Using plan recognition in human-computer collaboration , 1999 .

[28]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[29]  Michael P. Wellman,et al.  Accounting for Context in Plan Recognition, with Application to Traffic Monitoring , 1995, UAI.