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Plan, Activity, and Intent Recognition: Theory and Practice [Pehme köide]

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Edited by (Associate Professor, Drexel University), Edited by , Edited by (Principal Scientist, Laboratory for Natural Language Understanding, Nuance), Edited by (Assistant Professor, University of Central Florida), Edited by (Research Scientist, Institute for Creative Technologies, USC-)
  • Formaat: Paperback / softback, 424 pages, kõrgus x laius: 235x191 mm, kaal: 970 g
  • Ilmumisaeg: 10-Apr-2014
  • Kirjastus: Morgan Kaufmann Publishers In
  • ISBN-10: 0123985323
  • ISBN-13: 9780123985323
Teised raamatud teemal:
Plan, Activity, and Intent Recognition: Theory and PracticeSuurem pilt
  • Formaat: Paperback / softback, 424 pages, kõrgus x laius: 235x191 mm, kaal: 970 g
  • Ilmumisaeg: 10-Apr-2014
  • Kirjastus: Morgan Kaufmann Publishers In
  • ISBN-10: 0123985323
  • ISBN-13: 9780123985323
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780123985323.html
Märksõnad:
This collection of papers has to do with plan, activity, and intent recognition (PAIR) as a subfield of artificial intelligence (AI). The editors put the collection together at the 2013 PAIR conference. The book is technical, pointed at an audience of other scholars and practitioners in the field. It is divided into four parts: (1) classic plan- and goal-recognition approaches; (2) activity discovery from sensory data; (3) modeling human cognitive processes; (4) multiagent systems; and (5) practical applications. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com) 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 seminarsExplains how to interpret and recognize plans and activities from sensor dataProvides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines

Arvustused

"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." - HPCMagazine.com, 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)

Muu info

Gathers together core knowledge with the latest research and provides a single reference source for researchers
About the Editors xi
List of Contributors xiii
Preface xvii
Introduction xix
Part 1 Plan And Goal Recognition
Chapter 1 Hierarchical Goal Recognition
3(30)
1.1 Introduction
3(2)
1.2 Previous Work
5(1)
1.3 Data for Plan Recognition
6(4)
1.4 Metrics for Plan Recognition
10(2)
1.5 Hierarchical Goal Recognition
12(11)
1.6 System Evaluation
23(7)
1.7 Conclusion
30(1)
Acknowledgments
31(1)
References
31(2)
Chapter 2 Weighted Abduction for Discourse Processing Based on Integer linear Programming
33(24)
2.1 Introduction
33(1)
2.2 Related Work
34(1)
2.3 Weighted Abduction
35(1)
2.4 ILP-based Weighted Abduction
36(5)
2.5 Weighted Abduction for Plan Recognition
41(2)
2.6 Weighted Abduction for Discourse Processing
43(4)
2.7 Evaluation on Recognizing Textual Entailment
47(4)
2.8 Conclusion
51(1)
Acknowledgments
52(1)
References
52(5)
Chapter 3 Plan Recognition Using Statistical-Relational Models
57(30)
3.1 Introduction
57(2)
3.2 Background
59(2)
3.3 Adapting Bayesian Logic Programs
61(4)
3.4 Adapting Markov Logic
65(7)
3.5 Experimental Evaluation
72(9)
3.6 Future Work
81(1)
3.7 Conclusion
81(1)
Acknowledgments
82(1)
References
82(5)
Chapter 4 Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior
87(36)
4.1 Introduction
87(1)
4.2 Background: Adversarial Plan Recognition
88(5)
4.3 An Efficient Hybrid System for Adversarial Plan Recognition
93(6)
4.4 Experiments to Detect Anomalous and Suspicious Behavior
99(16)
4.5 Future Directions and Final Remarks
115(1)
Acknowledgments
116(1)
References
116(7)
Part 2 Activity Discovery And Recognition
Chapter 5 Stream Sequence Mining for Human Activity Discovery
123(26)
5.1 Introduction
123(2)
5.2 Related Work
125(4)
5.3 Proposed Model
129(9)
5.4 Experiments
138(5)
5.5 Conclusion
143(1)
References
144(5)
Chapter 6 Learning Latent Activities from Social Signals with Hierarchical Dirichlet Processes
149(28)
6.1 Introduction
149(1)
6.2 Related Work
150(4)
6.3 Bayesian Nonparametric Approach to Inferring Latent Activities
154(6)
6.4 Experiments
160(11)
6.5 Conclusion
171(1)
References
172(5)
Part 3 Modeling Human Cognition
Chapter 7 Modeling Human Plan Recognition Using Bayesian Theory of Mind
177(28)
7.1 Introduction
177(4)
7.2 Computational Framework
181(9)
7.3 Comparing the Model to Human Judgments
190(5)
7.4 Discussion
195(3)
7.5 Conclusion
198(1)
References
198(7)
Chapter 8 Decision-Theoretic Planning in Multiagent Settings with Application to Behavioral Modeling
205(22)
8.1 Introduction
205(1)
8.2 The Interactive POMDP Framework
206(4)
8.3 Modeling Deep, Strategic Reasoning by Humans Using I-POMDPs
210(11)
8.4 Discussion
221(1)
8.5 Conclusion
222(1)
Acknowledgments
222(1)
References
222(5)
Part 4 Multiagent Systems
Chapter 9 Multiagent Plan Recognition from Partially Observed Team Traces
227(24)
9.1 Introduction
227(1)
9.2 Preliminaries
228(2)
9.3 Multiagent Plan Recognition with Plan Library
230(5)
9.4 Multiagent Plan Recognition with Action Models
235(6)
9.5 Experiment
241(5)
9.6 Related Work
246(1)
9.7 Conclusion
247(1)
Acknowledgment
248(1)
References
248(3)
Chapter 10 Role-Based Ad Hoc Teamwork
251(24)
10.1 Introduction
251(1)
10.2 Related Work
252(3)
10.3 Problem Definition
255(2)
10.4 Importance of Role Recognition
257(1)
10.5 Models for Choosing a Role
258(5)
10.6 Model Evaluation
263(8)
10.7 Conclusion and Future Work
271(1)
Acknowledgments
272(1)
References
272(3)
Part 5 Applications
Chapter 11 Probabilistic Plan Recognition for Proactive Assistant Agents
275(14)
11.1 Introduction
275(1)
11.2 Proactive Assistant Agent
276(1)
11.3 Probabilistic Plan Recognition
277(5)
11.4 Plan Recognition within a Proactive Assistant System
282(2)
11.5 Applications
284(2)
11.6 Conclusion
286(1)
Acknowledgment
287(1)
References
287(2)
Chapter 12 Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks
289(24)
12.1 Introduction
289(2)
12.2 Related Work
291(2)
12.3 Observation Corpus
293(5)
12.4 Markov Logic Networks
298(2)
12.5 Goal Recognition with Markov Logic Networks
300(3)
12.6 Evaluation
303(3)
12.7 Discussion
306(3)
12.8 Conclusion and Future Work
309(1)
Acknowledgments
309(1)
References
309(4)
Chapter 13 Using Opponent Modeling to Adapt Team Play in American Football
313(30)
13.1 Introduction
313(2)
13.2 Related Work
315(2)
13.3 Rush Football
317(2)
13.4 Play Recognition Using Support Vector Machines
319(2)
13.5 Team Coordination
321(5)
13.6 Offline UCT for Learning Football Plays
326(4)
13.7 Online UCT for Multiagent Action Selection
330(9)
13.8 Conclusion
339(1)
Acknowledgment
339(1)
References
339(4)
Chapter 14 Intent Recognition for Human–Robot Interaction
343(24)
14.1 Introduction
343(1)
14.2 Previous Work in Intent Recognition
344(1)
14.3 Intent Recognition in Human–Robot Interaction
345(3)
14.4 HMM-Based Intent Recognition
348(1)
14.5 Contextual Modeling and Intent Recognition
349(7)
14.6 Experiments on Physical Robots
356(7)
14.7 Discussion
363(1)
14.8 Conclusion
364(1)
References
364(3)
Author Index 367(12)
Subject Index 379
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 UCFs 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 Sukthankars 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.