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