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J. Pearl (1988)
Probabilistic reasoning in intelligent systems
M Maragoudakis, A Thanopoulos, N Fakotakis (2003)
6th International conference on text, speech and dialogue (TSD 2003), Ceske Budejovice, Czech Republic
S. Carberry (1990)
Plan Recognition in Natural Language Dialogue
(2002)
Policy recognition in the Abstract Hidden Markov Model
(2000)
Keyhole state space recognition with applications to user modeling
M. Bauer (2007)
Acquisition of User Preferences for Plan Recognition
Oren Etzioni, N. Lesh (1998)
Scalable and adaptive goal recognition
J Breuker (1990)
EUROHELP: developing intelligent help systems
D. Pynadath, Michael Wellman (2000)
Probabilistic State-Dependent Grammars for Plan RecognitionArXiv, abs/1301.3888
(1990)
Conceptual model of intelligent help systems
D. Pynadath, Michael Wellman (1996)
Generalized Queries on Probabilistic Context-Free Grammars
Marcus Huber, E. Durfee, Michael Wellman (1994)
The Automated Mapping of Plans for Plan Recognition
Wray Buntine (1991)
Theory Refinement on Bayesian Networks
Zheng Chen, Fan Lin, Huan Liu, Wei-Ying Ma, Wenyin Liu (2002)
User Intention Modeling in Web Applications Using Data MiningWorld Wide Web, 5
BJ Grosz, S Kraus (1999)
Foundations and theories of rational agencies
M. Maragoudakis, Aristomenis Thanopoulos, N. Fakotakis (2003)
User Modeling and Plan Recognition under Conditions of Uncertainty
Yiming Yang, Jan Pedersen (1997)
A Comparative Study on Feature Selection in Text Categorization
J. Azarewicz, G. Fala, C. Heithecker (1989)
Template-based multi-agent plan recognition for tactical situation assessment[1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications
G Shafer (1976)
A mathematical theory of evidence
(1989)
A tutorial on hidden Markov models and selected applications in speech recognitionProc. IEEE, 77
Eugene Charniak, R. Goldman (1991)
A Probabilistic Model of Plan Recognition
C. Stanfill, D. Waltz (1986)
Toward memory-based reasoningCommun. ACM, 29
Eugene Charniak, R. Goldman (1989)
A Semantics for Probabilistic Quantifier-Free First-Order Languages, with Particular Application to Story Understanding
Annika Waern (1996)
Recognising Human Plans: Issues for Plan Recognition in Human - Computer Interaction
D. Albrecht, Ingrid Zukerman, A. Nicholson (2004)
Bayesian Models for Keyhole Plan Recognition in an Adventure GameUser Modeling and User-Adapted Interaction, 8
C. Rich, C. Sidner (1997)
COLLAGEN: when agents collaborate with people
Henry Kautz, James Allen (1986)
Generalized Plan Recognition
Henry Kautz (1987)
A formal theory of plan recognition
B. Grosz, Sarit Kraus (1996)
Collaborative Plans for Complex Group ActionArtif. Intell., 86
C. Schmidt, N. Sridharan, J. Goodson (1978)
The Plan Recognition Problem: An Intersection of Psychology and Artificial IntelligenceArtif. Intell., 11
Marcus Huber, R. Simpson (2004)
Recognizing the Plans of Screen Reader Users
P. Maes (1994)
Agents that reduce work and information overloadCommun. ACM, 37
C. Geib, R. Goldman (2005)
Partial Observability and Probabilistic Plan/Goal Recognition
C. Geib (2004)
Assessing the Complexity of Plan Recognition
N. Lesh, C. Rich, C. Sidner (1999)
Using plan recognition in human-computer collaboration
BJ Grosz, C Sidner (1990)
Intentions and communications
S. Carberry (2001)
Techniques for Plan RecognitionUser Modeling and User-Adapted Interaction, 11
Brian Davison, H. Hirsh (1998)
Predicting Sequences of User Actions
M. Maragoudakis, Aristomenis Thanopoulos, K. Sgarbas, N. Fakotakis (2003)
Domain knowledge acquisition and plan recognition by probabilistic reasoningInt. J. Artif. Intell. Tools, 13
C. Rich, C. Sidner, N. Lesh (2001)
COLLAGEN: Applying Collaborative Discourse Theory to Human-Computer InteractionAI Mag., 22
R. Goldman, C. Geib, C. Miller (1999)
A New Model of Plan RecognitionArXiv, abs/1301.6700
E. Durfee, Marcus Huber (1993)
Observational Uncertainty in Plan Recognition Among Interacting Robots
P. Spirtes, C. Glymour, R. Scheines (1993)
Causation, prediction, and search
N. Lesh, Oren Etzioni (1995)
A Sound and Fast Goal Recognizer
James Allen, Henry Kautz, R. Pelavin, J. Tenenberg (1991)
Reasoning about plans
Philip Cohen, J. Morgan, M. Pollack (2003)
Plans for Discourse
L. Ardissono, D. Sestero (2005)
Using dynamic user models in the recognition of the plans of the userUser Modeling and User-Adapted Interaction, 5
Charles Rich, C. Sidner (1998)
COLLAGEN: A Collaboration Manager for Software Interface AgentsUser Modeling and User-Adapted Interaction, 8
J. Greer, Gina Aries (1995)
The Peculiarities of Plan Recognition for Intelligent Tutoring Systems
Eugene Charniak, R. Goldman (1993)
A Bayesian Model of Plan RecognitionArtif. Intell., 64
J. Pearl (1991)
Probabilistic reasoning in intelligent systems - networks of plausible inference
E. Santos, Scott Brown (1998)
A decision theoretic approach for interface agent development
J. Quinlan (1986)
Induction of Decision TreesMachine Learning, 1
(1989)
User Model User-Adapt Interact
E. Horvitz, J. Breese, D. Heckerman, D. Hovel, Koos Rommelse (1998)
The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
B. Grosz, Sarit Kraus (1999)
The Evolution of Sharedplans
DV Pynadath, MP Wellman (2000)
UAI
H. Bui (2003)
A General Model for Online Probabilistic Plan Recognition
C Rich, CL Sidner, N Leash (2001)
COLLAGEN: applying collaborative discourse theory to human–computer interactionArtif Intell Mag, 22
M. Bauer (2005)
A dempster-shafer approach to modeling agent preferences for plan recognitionUser Modeling and User-Adapted Interaction, 5
HH Bui (2003)
IJCIA
G. Cooper, E. Herskovits (1992)
A Bayesian method for the induction of probabilistic networks from dataMachine Learning, 9
Interface agents are computer programs that provide personalized assistance to a user dealing with computer based applications. By understanding the tasks the user performs in a software application an interface agent could be aware of the context that represents the user’s focus of attention at each particular moment. With this purpose, plan recognition aims at identifying the plans or goals of a user from the tasks he (for simplicity, we use “he” to refer to the user, but we do not mean any distinctions about sexes) performs. A prerequisite for the recognition of plans is knowledge of a user’s possible tasks and the combination of these tasks in complex task sequences, which describes typical user behavior. Plan recognition will enable an interface agent to reason about what the user might do next so that it can determine how to assist him. In this work we present the state of the art in Plan Recognition, paying special attention to the features that make it useful to interface agents. These features include the ability to deal with uncertainty, multiple plans, multiple interleaved goals, overloaded tasks, noisy tasks, interruptions and the capability to adapt to a particular user.
Artificial Intelligence Review – Springer Journals
Published: Feb 11, 2009
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