Plan, Activity, and Intent Recognition - 1st Edition - ISBN: 9780123985323, 9780124017108

Plan, Activity, and Intent Recognition

1st Edition

Theory and Practice

Editors: Gita Sukthankar Christopher Geib Hung Hai Bui David Pynadath Robert Goldman
eBook ISBN: 9780124017108
Paperback ISBN: 9780123985323
Imprint: Morgan Kaufmann
Published Date: 24th February 2014
Page Count: 424
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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.

Key Features

  • Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminars
  • Explains how to interpret and recognize plans and activities from sensor data
  • Provides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines


Academic researchers and industrial researchers in specific application areas such as user interface design and video surveillance systems.

Table of Contents

About the Editors




1: Plan and Goal Recognition

1: Hierarchical Goal Recognition

1.1 Introduction

1.2 Previous Work

1.3 Data for Plan Recognition

1.4 Metrics for Plan Recognition

1.5 Hierarchical Goal Recognition

1.6 System Evaluation

1.7 Conclusion

2: Weighted Abduction for Discourse Processing Based on Integer Linear Programming

2.1 Introduction

2.2 Related Work

2.3 Weighted Abduction

2.4 ILP-based Weighted Abduction

2.5 Weighted Abduction for Plan Recognition

2.6 Weighted Abduction for Discourse Processing

2.7 Evaluation on Recognizing Textual Entailment

2.8 Conclusion

3: Plan Recognition Using Statistical–Relational Models

3.1 Introduction

3.2 Background

3.3 Adapting Bayesian Logic Programs

3.4 Adapting Markov Logic

3.5 Experimental Evaluation

3.6 Future Work

3.7 Conclusion

4: Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior

4.1 Introduction

4.2 Background: Adversarial Plan Recognition

4.3 An Efficient Hybrid System for Adversarial Plan Recognition

4.4 Experiments to Detect Anomalous and Suspicious Behavior

4.5 Future Directions and Final Remarks

2: Activity Discovery and Recognition

5: Stream Sequence Mining for Human Activity Discovery

5.1 Introduction

5.2 Related Work

5.3 Proposed Model

5.4 Experiments

5.5 Conclusion

6: Learning Latent Activities from Social Signals with Hierarchical Dirichlet Processes

6.1 Introduction

6.2 Related Work

6.3 Bayesian Nonparametric Approach to Inferring Latent Activities

6.4 Experiments

6.5 Conclusion

3: Modeling Human Cognition

7: Modeling Human Plan Recognition Using Bayesian Theory of Mind

7.1 Introduction

7.2 Computational Framework

7.3 Comparing the Model to Human Judgments

7.4 Discussion

7.5 Conclusion

8: Decision-Theoretic Planning in Multiagent Settings with Application to Behavioral Modeling

8.1 Introduction

8.2 The Interactive POMDP Framework

8.3 Modeling Deep, Strategic Reasoning by Humans Using I-POMDPs

8.4 Discussion

8.5 Conclusion

4: Multiagent Systems

9: Multiagent Plan Recognition from Partially Observed Team Traces

9.1 Introduction

9.2 Preliminaries

9.3 Multiagent Plan Recognition with Plan Library

9.4 Multiagent Plan Recognition with Action Models

9.5 Experiment

9.6 Related Work

9.7 Conclusion

10: Role-Based Ad Hoc Teamwork

10.1 Introduction

10.2 Related Work

10.3 Problem Definition

10.4 Importance of Role Recognition

10.5 Models for Choosing a Role

10.6 Model Evaluation

10.7 Conclusion and Future Work

5: Applications

11: Probabilistic Plan Recognition for Proactive Assistant Agents

11.1 Introduction

11.2 Proactive Assistant Agent

11.3 Probabilistic Plan Recognition

11.4 Plan Recognition within a Proactive Assistant System

11.5 Applications

11.6 Conclusion

12: Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks

12.1 Introduction

12.2 Related Work

12.3 Observation Corpus

12.4 Markov Logic Networks

12.5 Goal Recognition with Markov Logic Networks

12.6 Evaluation

12.7 Discussion

12.8 Conclusion and Future Work

13: Using Opponent Modeling to Adapt Team Play in American Football

13.1 Introduction

13.2 Related Work

13.3 Rush Football

13.4 Play Recognition Using Support Vector Machines

13.5 Team Coordination

13.6 Offline UCT for Learning Football Plays

13.7 Online UCT for Multiagent Action Selection

13.8 Conclusion

14: Intent Recognition for Human–Robot Interaction

14.1 Introduction

14.2 Previous Work in Intent Recognition

14.3 Intent Recognition in Human–Robot Interaction

14.4 HMM-Based Intent Recognition

14.5 Contextual Modeling and Intent Recognition

14.6 Experiments on Physical Robots

14.7 Discussion

14.8 Conclusion

Author Index

Subject Index


No. of pages:
© Morgan Kaufmann 2014
Morgan Kaufmann
eBook ISBN:
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About the Editor

Gita Sukthankar

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.

Affiliations and Expertise

Assistant Professor, University of Central Florida

Christopher Geib

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.

Affiliations and Expertise

Associate Professor, Drexel University

Hung Hai Bui

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.

Affiliations and Expertise

Principal Scientist, Laboratory for Natural Language Understanding, Nuance

David Pynadath

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.

Affiliations and Expertise

Research Scientist, Institute for Creative Technologies, USC

Robert Goldman

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.

Affiliations and Expertise

Staff Scientist, Smart Information Flow Technologies LLC


"This book serves to provide a coherent snapshot of the exciting developments in the field enabled by improved sensors, increased computational power, and new application areas." -, August 2014


"Plan recognition, activity recognition, and intent recognition all involve making inferences about other actors from observations of their behavior. These inferences are crucial in a wide range of applications including intelligent assistants, computer security, and dialogue management systems. This volume, edited by leading researchers, provides a timely snapshot of some of the key formulations, techniques, and applications that have been developed in this rich and rapidly evolving field."

–Dr. Hector Geffner, ICREA & Universitat Pompeu Fabra, Barcelona

"This book collects some of the top senior people in the field of plan recognition with some of the newest researchers. It offers a comprehensive review of plan recognition from multiple viewpoints, encompassing both logical and probabilistic formalisms and covering mathematical theory, computer science applications, and human cognitive models."

–Dr. Peter Norvig, Director of Research at Google Inc.

"Plan, Activity, and Intent Recognition is an indispensable resource for creating systems that infer peoples’ goals and plans on the basis of their behavior. Researchers in security, natural language dialog systems, smart spaces and pervasive computing, and other areas will find a comprehensive and up to date survey of methods, applications, and open research challenges."

–Dr. Henry Kautz, University of Rochester, Past President of AAAI (Association for the Advancement of Artificial Intelligence)