Plan, Activity, and Intent Recognition
Theory and Practice
- 1st Edition - February 24, 2014
- Latest edition
- Editors: Gita Sukthankar, Christopher Geib, Hung Hai Bui, David Pynadath, Robert P. Goldman
- Language: English
Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, hu… Read more
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.
- 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
About the Editors
Contributors
Preface
Introduction
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
- Edition: 1
- Latest edition
- Published: February 24, 2014
- Language: English
GS
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 FloridaCG
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 UniversityHB
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, NuanceDP
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, USCRG
Robert P. 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 LLCRead Plan, Activity, and Intent Recognition on ScienceDirect