Librería Portfolio

Key Features

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
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.
Readership

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



Plan, Activity, and Intent Recognition, 1st Edition

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