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Plan, Activity, and Intent Recognition

Book Description

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

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. About the Editors
  6. Contributors
  7. Preface
  8. Introduction
  9. 1: Plan and Goal Recognition
    1. 1: Hierarchical Goal Recognition
      1. 1.1 Introduction
      2. 1.2 Previous Work
      3. 1.3 Data for Plan Recognition
      4. 1.4 Metrics for Plan Recognition
      5. 1.5 Hierarchical Goal Recognition
      6. 1.6 System Evaluation
      7. 1.7 Conclusion
    2. 2: Weighted Abduction for Discourse Processing Based on Integer Linear Programming
      1. 2.1 Introduction
      2. 2.2 Related Work
      3. 2.3 Weighted Abduction
      4. 2.4 ILP-based Weighted Abduction
      5. 2.5 Weighted Abduction for Plan Recognition
      6. 2.6 Weighted Abduction for Discourse Processing
      7. 2.7 Evaluation on Recognizing Textual Entailment
      8. 2.8 Conclusion
    3. 3: Plan Recognition Using Statistical–Relational Models
      1. 3.1 Introduction
      2. 3.2 Background
      3. 3.3 Adapting Bayesian Logic Programs
      4. 3.4 Adapting Markov Logic
      5. 3.5 Experimental Evaluation
      6. 3.6 Future Work
      7. 3.7 Conclusion
    4. 4: Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior
      1. 4.1 Introduction
      2. 4.2 Background: Adversarial Plan Recognition
      3. 4.3 An Efficient Hybrid System for Adversarial Plan Recognition
      4. 4.4 Experiments to Detect Anomalous and Suspicious Behavior
      5. 4.5 Future Directions and Final Remarks
  10. 2: Activity Discovery and Recognition
    1. 5: Stream Sequence Mining for Human Activity Discovery
      1. 5.1 Introduction
      2. 5.2 Related Work
      3. 5.3 Proposed Model
      4. 5.4 Experiments
      5. 5.5 Conclusion
    2. 6: Learning Latent Activities from Social Signals with Hierarchical Dirichlet Processes
      1. 6.1 Introduction
      2. 6.2 Related Work
      3. 6.3 Bayesian Nonparametric Approach to Inferring Latent Activities
      4. 6.4 Experiments
      5. 6.5 Conclusion
  11. 3: Modeling Human Cognition
    1. 7: Modeling Human Plan Recognition Using Bayesian Theory of Mind
      1. 7.1 Introduction
      2. 7.2 Computational Framework
      3. 7.3 Comparing the Model to Human Judgments
      4. 7.4 Discussion
      5. 7.5 Conclusion
    2. 8: Decision-Theoretic Planning in Multiagent Settings with Application to Behavioral Modeling
      1. 8.1 Introduction
      2. 8.2 The Interactive POMDP Framework
      3. 8.3 Modeling Deep, Strategic Reasoning by Humans Using I-POMDPs
      4. 8.4 Discussion
      5. 8.5 Conclusion
  12. 4: Multiagent Systems
    1. 9: Multiagent Plan Recognition from Partially Observed Team Traces
      1. 9.1 Introduction
      2. 9.2 Preliminaries
      3. 9.3 Multiagent Plan Recognition with Plan Library
      4. 9.4 Multiagent Plan Recognition with Action Models
      5. 9.5 Experiment
      6. 9.6 Related Work
      7. 9.7 Conclusion
    2. 10: Role-Based Ad Hoc Teamwork
      1. 10.1 Introduction
      2. 10.2 Related Work
      3. 10.3 Problem Definition
      4. 10.4 Importance of Role Recognition
      5. 10.5 Models for Choosing a Role
      6. 10.6 Model Evaluation
      7. 10.7 Conclusion and Future Work
  13. 5: Applications
    1. 11: Probabilistic Plan Recognition for Proactive Assistant Agents
      1. 11.1 Introduction
      2. 11.2 Proactive Assistant Agent
      3. 11.3 Probabilistic Plan Recognition
      4. 11.4 Plan Recognition within a Proactive Assistant System
      5. 11.5 Applications
      6. 11.6 Conclusion
    2. 12: Recognizing Player Goals in Open-Ended Digital Games with Markov Logic Networks
      1. 12.1 Introduction
      2. 12.2 Related Work
      3. 12.3 Observation Corpus
      4. 12.4 Markov Logic Networks
      5. 12.5 Goal Recognition with Markov Logic Networks
      6. 12.6 Evaluation
      7. 12.7 Discussion
      8. 12.8 Conclusion and Future Work
    3. 13: Using Opponent Modeling to Adapt Team Play in American Football
      1. 13.1 Introduction
      2. 13.2 Related Work
      3. 13.3 Rush Football
      4. 13.4 Play Recognition Using Support Vector Machines
      5. 13.5 Team Coordination
      6. 13.6 Offline UCT for Learning Football Plays
      7. 13.7 Online UCT for Multiagent Action Selection
      8. 13.8 Conclusion
    4. 14: Intent Recognition for Human–Robot Interaction
      1. 14.1 Introduction
      2. 14.2 Previous Work in Intent Recognition
      3. 14.3 Intent Recognition in Human–Robot Interaction
      4. 14.4 HMM-Based Intent Recognition
      5. 14.5 Contextual Modeling and Intent Recognition
      6. 14.6 Experiments on Physical Robots
      7. 14.7 Discussion
      8. 14.8 Conclusion
  14. Author Index
  15. Subject Index