Description
Plan, Activity, and Intent Recognition
Theory and Practice
Coordinators: Sukthankar Gita, Geib Christopher, Bui Hung Hai, Pynadath David, Goldman Robert P.
Language: EnglishSubject for Plan, Activity, and Intent Recognition:
Keywords
PAIR; plan recognition; activity recognition; artificial intelligence; machine learning; human computer interaction (HCI); textbook; probabilistic inference; abductive reasoning; Bayesian models; human behavior modeling; smart homes; intelligent user interfaces; assistive interfaces; multi-agent systems; artificial intelligence in computer games
424 p. · 19x23.3 cm · Paperback
Description
/li>Contents
/li>Readership
/li>Biography
/li>Comment
/li>
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.
Plan and Goal Recognition 1. Hierarchical Goal Recognition 2. Weighted Abduction for Discourse Processing Based on Integer Linear Programming 3. Plan Recognition using Statistical Relational Models 4. Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior
Activity Discovery and Recognition 5. Scaling Activity Recognition 6. Extraction of Latent Patterns and Contexts from Social Honest Signals Using Hierarchical Dirichlet Processes
Modeling Human Cognition 7. Modeling Human Plan Recognition using Bayesian Theory of Mind 8. Decision Theoretic Planning in Multiagent Settings with Application to Modeling Human Strategic Behavior
Multiagent Systems 9. Multiagent Plan Recognition from Partially Observed Team Traces 10. Role-based Ad Hoc Teamwork
Applications 11. Probabilistic plan recognition for proactive assistant agents 12. Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks 13. Using Opponent Modeling to Adapt Team Play in American Football 14. Intent Recognition for Human-Robot Interaction
Academic researchers and industrial researchers in specific application areas such as user interface design and video surveillance systems.
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 Institu
- 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