Plan, Activity, and Intent Recognition. Theory and Practice PDF
Preview Plan, Activity, and Intent Recognition. Theory and Practice
Plan, Activity, and Intent Recognition Plan, Activity, and Intent Recognition Theory and Practice Edited by Gita Sukthankar Robert P. Goldman Christopher Geib David V. Pynadath Hung Hai Bui AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is an imprint of Elsevier Acquiring Editor: Todd Green Editorial Project Manager: Lindsay Lawrence Project Manager: Punithavathy Govindaradjane Designer: Russell Purdy Morgan Kaufmann is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA Copyright © 2014 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our under- standing, changes in research methods or professional practices, may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information or methods described herein. In using such information or methods they should be mindful of their own safety and the safety of others, includ- ing parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data Plan, activity, and intent recognition / Gita Sukthankar, Robert P. Goldman, Christopher Geib, David V. Pynadath, Hung Hai Bui. pages cm. ISBN 978-0-12-398532-3 1. Human activity recognition. 2. Artificial intelligence. 3. Pattern perception. 4. Intention. I. Sukthankar, Gita, editor of compilation. TK7882.P7P57 2014 006.3--dc23 2013050370 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-398532-3 Printed and bound in the United States of America 14 15 16 17 18 10 9 8 7 6 5 4 3 2 1 For information on all MK publications visit our website at www.mkp.com About the Editors 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, where she researched multiagent plan recognition algo- rithms. 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 multiagent systems and computational social models. 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, he was an 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. Christopher Geib is an Associate Professor in the College of Computing and Informatics at Drexel University. Before joining Drexel, Professor 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 Postdoctoral 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 more than 20 years. Dr. David V. Pynadath is a Research Scientist at the University of Southern California’s Institute for Creative Technologies. He received his Ph.D. in computer science from the University of Michigan in 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. Dr. Hung Hai Bui is a Principal Research Scientist at the Laboratory for Natural Language Understanding, Nuance in Sunnyvale, CA. His main research interests include probabilistic reasoning and machine learning and their application in plan and activity recognition. Before joining Nuance, he spent nine years as a Senior Computer Scientist at SRI International, where he led several multi- institutional 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 in Western Australia. xi List of Contributors Noa Agmon Bar Ilan University, Ramat Gan, Israel James Allen Florida Institute for Human and Machine Cognition, Pensacola, FL, USA Amol Ambardekar University of Nevada, Reno, NV, USA Dorit Avrahami-Zilberbrand Bar Ilan University, Ramat Gan, Israel Chris L. Baker Massachusetts Institute of Technology, Cambridge, MA, USA Nate Blaylock Nuance Communications, Montreal, QC, Canada Prashant Doshi University of Georgia, Athens, GA, USA Katie Genter University of Texas at Austin, Austin, TX, USA Adam Goodie University of Georgia, Athens, GA, USA Sunil Gupta Deakin University, Waurn Ponds, VIC, Australia Eun Y. Ha North Carolina State University, Raleigh, NC, USA Jerry Hobbs USC/ISI, Marina del Rey, CA, USA Naoya Inoue Tohoku University, Sendai, Japan Kentaro Inui Tohoku University, Sendai, Japan Gal A. Kaminka Bar Ilan University, Ramat Gan, Israel Richard Kelley University of Nevada, Reno, NV, USA Christopher King University of Nevada, Reno, NV, USA xiii xiv List of Contributors Kennard R. Laviers Air Force Institute of Technology, Wright Patterson AFB, OH, USA James C. Lester North Carolina State University, Raleigh, NC, USA Felipe Meneguzzi Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil Raymond J. Mooney University of Texas at Austin, Austin, TX, USA Bradford W. Mott North Carolina State University, Raleigh, NC, USA Thuong Nguyen Deakin University, Waurn Ponds, VIC, Australia Mircea Nicolescu University of Nevada, Reno, NV, USA Monica Nicolescu University of Nevada, Reno, NV, USA Jean Oh Carnegie Mellon University, Pittsburgh, PA, USA Ekaterina Ovchinnikova USC/ISI, Marina del Rey, CA, USA Dinh Phung Deakin University, Waurn Ponds, VIC, Australia Xia Qu University of Georgia, Athens, GA, USA Sindhu Raghavan University of Texas at Austin, Austin, TX, USA Parisa Rashidi University of Florida, Gainesville, FL, USA Jonathan P. Rowe North Carolina State University, Raleigh, NC, USA Parag Singla Indian Institute of Technology Delhi, Hauz Khas, DL, India Peter Stone University of Texas at Austin, Austin, TX, USA Gita Sukthankar University of Central Florida, Orlando, FL, USA List of Contributors xv Katia Sycara Carnegie Mellon University, Pittsburgh, PA, USA Alireza Tavakkoli University of Nevada, Reno, NV, USA Joshua B. Tenenbaum Massachusetts Institute of Technology, Cambridge, MA, USA Svetha Venkatesh Deakin University, Waurn Ponds, VIC, Australia Liesl Wigand University of Nevada, Reno, NV, USA Hankz Hankui Zhuo Sun Yat-sen University, Guangzhou, China Preface The diversity of applications and disciplines encompassed by the subfi eld of plan, intent, and activity recognition, while producing a wealth of ideas and results, has unfortunately contributed to fragmen- tation in the area because researchers present relevant results in a broad spectrum of journals and at conferences. This book serves to provide a coherent snapshot of the exciting developments in the fi eld enabled by improved sensors, increased computational power, and new application areas. While the individual chapters are motivated by different applications and employ diverse technical approaches, they are unifi ed by the ultimate task of understanding another agent’s behaviors. As there is not yet a single common conference for this growing fi eld, we hope that this book will serve as a valuable resource for researchers interested in learning about work originating from other communities. The editors have organized workshops in this topic area at the following artifi cial intel- ligence conferences since 2004: • Modeling Other Agents From Observations (MOO 2004) at the International Conference on Autonomous Agents and Multi-agent Systems, AAMAS-2004, organized by Gal Kaminka, Piotr Gmytrasiewicz, David Pynadath, and Mathias Bauer • Modeling Other Agents From Observations (MOO 2005) at the International Joint Conference on Artifi cial Intelligence, IJCAI-2005, organized by Gal Kaminka, David Pynadath, and Christopher Geib • Modeling Other Agents From Observations (MOO 2006) at the National Conference on Artifi cial Intelligence, AAAI-2006, organized by Gal Kaminka, David Pynadath, and Christopher Geib • Plan, Activity, and Intent Recognition (PAIR 2007) at the National Conference on Artifi cial Intelligence, AAAI-2007, organized by Christopher Geib and David Pynadath • Plan, Activity, and Intent Recognition (PAIR 2009) at the International Joint Conference on Artifi cial Intelligence, IJCAI-2009, organized by Christopher Geib, David Pynadath, Hung Bui, and Gita Sukthankar • Plan, Activity, and Intent Recognition (PAIR 2010) at the National Conference on Artifi cial Intelligence, AAAI-2010, organized by Gita Sukthankar, Christopher Geib, David Pynadath, and Hung Bui • Plan, Activity, and Intent Recognition (PAIR 2011) at the National Conference on Artifi cial Intelligence, AAAI-2011, organized by Gita Sukthankar, Hung Bui, Christopher Geib, and David Pynadath • Dagstuhl Seminar on Plan Recognition in Dagstuhl, Germany, organized by Tanim Asfour, Christopher Geib, Robert Goldman, and Henry Kautz • Plan, Activity, and Intent Recognition (PAIR 2013) at the National Conference on Artifi cial Intelligence, AAAI-2013, organized by Hung Bui, Gita Sukthankar, Christopher Geib, and David Pynadath T he editors and many of the authors gathered together at the 2013 PAIR workshop to put the fi n- ishing touches on this book, which contains some of the best contributions from the community. We thank all of the people who have participated in these events over the years for their interesting research presentations, exciting intellectual discussions, and great workshop dinners (see Figure P.1 ). xvii xviii Preface FIGURE P.1 Tag cloud created from the titles of papers that have appeared at the workshops in this series. CHAPTER Introduction Overview The ability to recognize the plans and goals of other agents enables humans to reason about what otherpeoplearedoing,whytheyaredoingit,andwhattheywilldonext.Thisfundamentalcognitive capabilityisalsocriticaltointerpersonalinteractionsbecausehumancommunicationspresupposean abilitytounderstandthemotivationsoftheparticipantsandsubjectsofthediscussion.Asthecomplexity ofhuman–machineinteractionsincreasesandautomatedsystemsbecomemoreintelligent,westriveto providecomputerswithcomparableintent-recognitioncapabilities. Research addressing this area is variously referred to as plan recognition, activity recognition, goal recognition, and intent recognition. This synergistic research area combines techniques from usermodeling,computervision,naturallanguageunderstanding,probabilisticreasoning,andmachine learning. Plan-recognition algorithms play a crucial role in a wide variety of applications including smarthomes,intelligentuserinterfaces,personalagentassistants,human–robotinteraction,andvideo surveillance. Plan-recognitionresearchincomputersciencedatesbackatleast35years;itwasinitiallydefined in a paper by Schmidt, Sridharan, and Goodson [64]. In the last ten years, significant advances have beenmadeonthissubjectbyresearchersinartificialintelligence(AI)andrelatedareas.Theseadvances have been driven by three primary factors: (1) the pressing need for sophisticated and efficient plan- recognition systems for a wide variety of applications; (2) the development of new algorithmic techniquesinprobabilisticmodeling,machinelearning,andoptimization(combinedwithmorepow- erful computers to use these techniques); and (3) our increased ability to gather data about human activities. Recentresearchinthefieldisoftendividedintotwosubareas.Activityrecognitionfocusesonthe problem of dealing directly with noisy low-level data gathered by physical sensors such as cameras, wearable sensors,and instrumented user interfaces. The primary taskinthis space isto discover and extract interesting patterns in noisy sensory data that can be interpreted as meaningful activities. For example,anactivity-recognitionsystemprocessingasequenceofvideoframesmightstartbyextracting aseriesof motions andthen willattempttoverifythatthey areallpartoftheactivity offillingatea kettle. Plan and intent recognition concentrates on identifying high-level complex goals and intents byexploitingrelationshipsbetweenprimitiveactionstepsthatareelementsoftheplan.Relationships thathavebeeninvestigatedincludecausality,temporalordering,coordinationamongmultiplesubplans (possiblyinvolvingmultipleactors),andsocialconvention. xix
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