Plan, Activity, and Intent Recognition

Theory and Practice
Erscheinungsjahr 2014
ISBN: 978-0-12-398532-3
Verlag: Elsevier Science & Technology

Buch, Englisch, 424 Seiten, Format (B × H): 195 mm x 234 mm, Gewicht: 852 g

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.

Zielgruppe

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

Autoren/Hrsg.

Sukthankar, Gita

Geib, Christopher

Bui, Hung Hai

Pynadath, David

Goldman, Robert P.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion

Inhaltsverzeichnis

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

Über Autor(innen)

Goldman, Robert P.
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.

Sukthankar, Gita
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.

Geib, Christopher
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.

Bui, Hung Hai
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.

Pynadath, David
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.