Plan, activity, and intent recognition : theory and practice

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Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning.

Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including:

personal agent assistants computer and network security opponent modeling in games and simulation systems coordination in robots and software agents web e-commerce and collaborative filtering dialog modeling video surveillance smart homes

In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas.

Author Notes

Dr. Gita Sukthankar is an Associate Professor and Charles N. Millican Faculty Fellow in the Department of Electrical Engineering and Computer Science at the University of Central Florida, and an affiliate faculty member at UCF's Institute for Simulation and Training. She received her Ph.D. from the Robotics Institute at Carnegie Mellon, an M.S. in Robotics, and an A.B. in psychology from Princeton University. In 2009, Dr. Sukthankar was selected for an Air Force Young Investigator award, the DARPA Computer Science Study Panel, and an NSF CAREER award. Gita Sukthankar's research focuses on multi-agent systems and computational social models.

Christopher Geib is an Associate Professor in the College of Computing and Informatics at Drexel University. Before joining Drexel, Prof. Geib's career has spanned a number of academic and industrial posts including being a Research Fellow in the School of Informatics at the University of Edinburgh, a Principal Research Scientist working at Honeywell Labs, and a Post Doctoral Fellow at the University of British Columbia in the Laboratory for Computational Intelligence. He received his Ph.D. in Computer Science from the University of Pennsylvania and has worked on plan recognition and planning for over 20 years.

Dr. Hung Bui is a Principal Research Scientist at the Laboratory for Natural Language Understanding, Nuance, Sunnyvale, CA. His main research interests include probabilistic reasoning, machine learning and their applications in plan and activity recognition. Before joining Nuance, he spent 9 years as a senior computer scientist at SRI International, where he led several multi-institution research teams developing probabilistic inference technologies for understanding human activities and building personal intelligent assistants. He received his Ph.D. in Computer Science in 1998 from Curtin University, Western Australia.

Dr. David V. Pynadath is a Research Scientist at the University of Southern California Institute for Creative Technologies. He received his Ph.D. in Computer Science from the University of Michigan, Ann Arbor, where he studied probabilistic grammars for plan recognition. He was subsequently a Research Scientist at the USC Information Sciences Institute, and is currently a member of the Social Simulation Lab at USC ICT, where he conducts research in multiagent decision-theoretic methods for social reasoning.

Robert P. Goldman is a Staff Scientist at SIFT, LLC, specializing in Artificial Intelligence. Dr. Goldman received his Ph.D. in Computer Science from Brown University, where he worked on the first Bayesian model for plan recognition. Prior to joining SIFT, Dr. Goldman was Assistant Professor of Computer Science at Tulane University, and then Principal Research Scientist at Honeywell Labs. Dr. Goldman's research interests involve plan recognition, the intersection between planning, control theory, and formal methods, computer security, and reasoning under uncertainty.

About the Editors	p. xi
List of Contributors	p. xiii
Preface	p. xvii
Introduction	p. xix
Part 1 Plan and Goal Recognition
Chapter 1 Hierarchical Goal Recognition	p. 3
1.1 Introduction	p. 3
1.2 Previous Work	p. 5
1.3 Data for Plan Recognition	p. 6
1.4 Metrics for Plan Recognition	p. 10
1.5 Hierarchical Goal Recognition	p. 12
1.6 System Evaluation	p. 23
1.7 Conclusion	p. 30
Acknowledgments	p. 31
References	p. 31
Chapter 2 Weighted Abduction for Discourse Processing Based on Integer Linear Programming	p. 33
2.1 Introduction	p. 33
2.2 Related Work	p. 34
2.3 Weighted Abduction	p. 35
2.4 ILP-based Weighted Abduction	p. 36
2.5 Weighted Abduction for Plan Recognition	p. 41
2.6 Weighted Abduction for Discourse Processing	p. 43
2.7 Evaluation on Recognizing Textual Entailment	p. 47
2.8 Conclusion	p. 51
Acknowledgments	p. 52
References	p. 52
Chapter 3 Plan Recognition Using Statistical-Relational Models	p. 57
3.1 Introduction	p. 57
3.2 Background	p. 59
3.3 Adapting Bayesian Logic Programs	p. 61
3.4 Adapting Markov Logic	p. 65
3.5 Experimental Evaluation	p. 72
3.6 Future Work	p. 81
3.7 Conclusion	p. 81
Acknowledgments	p. 82
References	p. 82
Chapter 4 Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior	p. 87
4.1 Introduction	p. 87
4.2 Background: Adversarial Plan Recognition	p. 88
4.3 An Efficient Hybrid System for Adversarial Plan Recognition	p. 93
4.4 Experiments to Detect Anomalous and Suspicious Behavior	p. 99
4.5 Future Directions and Final Remarks	p. 115
Acknowledgments	p. 116
References	p. 116
Part 2 Activity Discovery and Recognition
Chapter 5 Stream Sequence Mining for Human Activity Discovery	p. 123
5.1 Introduction	p. 123
5.2 Related Work	p. 125
5.3 Proposed Model	p. 129
5.4 Experiments	p. 138
5.5 Conclusion	p. 143
References	p. 144
Chapter 6 Learning Latent Activities from Social Signals with Hierarchical Dirichlet Processes	p. 149
6.1 Introduction	p. 149
6.2 Related Work	p. 150
6.3 Bayesian Nonparametric Approach to Inferring Latent Activities	p. 154
6.4 Experiments	p. 160
6.5 Conclusion	p. 171
References	p. 172
Part 3 Modeling Human Cognition
Chapter 7 Modeling Human Plan Recognition Using Bayesian Theory of Mind	p. 177
7.1 Introduction	p. 177
7.2 Computational Framework	p. 181
7.3 Comparing the Model to Human Judgments	p. 190
7.4 Discussion	p. 195
7.5 Conclusion	p. 198
References	p. 198
Chapter 8 Decision-Theoretic Planning in Multiagent Settings with Application to Behavioral Modeling	p. 205
8.1 Introduction	p. 205
8.2 The Interactive POMDP Framework	p. 206
8.3 Modeling Deep, Strategic Reasoning by Humans Using I-POMDPs	p. 210
8.4 Discussion	p. 221
8.5 Conclusion	p. 222
Acknowledgments	p. 222
References	p. 222
Part 4 Multiagent Systems
Chapter 9 Multiagent Plan Recognition from Partially Observed Team Traces	p. 227
9.1 Introduction	p. 227
9.2 Preliminaries	p. 228
9.3 Multiagent Plan Recognition with Plan Library	p. 230
9.4 Multiagent Plan Recognition with Action Models	p. 235
9.5 Experiment	p. 241
9.6 Related Work	p. 246
9.7 Conclusion	p. 247
Acknowledgment	p. 248
References	p. 248
Chapter 10 Role-Based Ad Hoc Teamwork	p. 251
10.1 Introduction	p. 251
10.2 Related Work	p. 252
10.3 Problem Definition	p. 255
10.4 Importance of Role Recognition	p. 257
10.5 Models for Choosing a Role	p. 258
10.6 Model Evaluation	p. 263
10.7 Conclusion and Future Work	p. 271
Acknowledgments	p. 272
References	p. 272
Part 5 Applications
Chapter 11 Probabilistic Plan Recognition for Proactive Assistant Agents	p. 275
11.1 Introduction	p. 275
11.2 Proactive Assistant Agent	p. 276
11.3 Probabilistic Plan Recognition	p. 277
11.4 Plan Recognition within a Proactive Assistant System	p. 282
11.5 Applications	p. 284
11.6 Conclusion	p. 286
Acknowledgment	p. 287
References	p. 287
Chapter 12 Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks	p. 289
12.1 Introduction	p. 289
12.2 Related Work	p. 291
12.3 Observation Corpus	p. 293
12.4 Markov Logic Networks	p. 298
12.5 Goal Recognition with Markov Logic Networks	p. 300
12.6 Evaluation	p. 303
12.7 Discussion	p. 306
12.8 Conclusion and Future Work	p. 309
Acknowledgments	p. 309
References	p. 309
Chapter 13 Using Opponent Modeling to Adapt Team Play in American Football	p. 313
13.1 Introduction	p. 313
13.2 Related Work	p. 315
13.3 Rush Football	p. 317
13.4 Play Recognition Using Support Vector Machines	p. 319
13.5 Team Coordination	p. 321
13.6 Offline UCT for Learning Football Plays	p. 326
13.7 Online UCT for Multiagent Action Selection	p. 330
13.8 Conclusion	p. 339
Acknowledgment	p. 339
References	p. 339
Chapter 14 Intent Recognition for Human-Robot Interaction	p. 343
14.1 Introduction	p. 343
14.2 Previous Work in Intent Recognition	p. 344
14.3 Intent Recognition in Human-Robot Interaction	p. 345
14.4 HMM-Based Intent Recognition	p. 348
14.5 Contextual Modeling and Intent Recognition	p. 349
14.6 Experiments on Physical Robots	p. 356
14.7 Discussion	p. 363
14.8 Conclusion	p. 364
References	p. 364
Author Index	p. 367
Subject Index	p. 379

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