Multi-Agent Search under Uncertainty
Autor Barouch Matzliach, Evgeny Kagan, Irad Ben-Galen Limba Engleză Hardback – 16 sep 2026
When multiple robots must locate targets in presence of false positive and false negative detection errors, path planning becomes extraordinarily complex. Multi-Agent Search under Uncertainty addresses this challenge directly. Written by researchers with combined expertise spanning defense systems, applied mathematics, and machine learning, this book delivers both theoretical foundations in search and screening theory and ready-to-use algorithms for practical implementation.
The book covers cooperative search and navigation methods for autonomous mobile agents operating with incomplete or noisy information. Readers learn how Deep Q-Learning enables robots to develop complex behaviors through trial-and-error interactions rather than pre-programmed instructions. Applications span search and rescue operations, military surveillance, environmental monitoring, and security systems. An accompanying website provides Python code for simulation practice.
Key topics include:
- Value-based Q-Learning methods where robots learn expected rewards for specific actions in given states under sensor uncertainty conditions
- Multi-agent reinforcement learning approaches for swarm robotics where multiple robots learn cooperatively to accomplish collaborative search tasks
- Deep reinforcement learning using neural networks to process high-dimensional sensory inputs and execute complex search and tracking behaviors
- Algorithms for finding and tracking both stationary and moving targets while minimizing detection time despite false negative and positive readings
- Theoretical contributions to search and screening theory alongside practical algorithms validated in autonomous robotic systems development
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Specificații
ISBN-13: 9781394418459
ISBN-10: 1394418450
Pagini: 128
Dimensiuni: 178 x 254 x 10 mm
Greutate: 0.45 kg
Editura: John Wiley & Sons, Inc.
ISBN-10: 1394418450
Pagini: 128
Dimensiuni: 178 x 254 x 10 mm
Greutate: 0.45 kg
Editura: John Wiley & Sons, Inc.
Notă biografică
Barouch Matzliach, PhD, is a Lecturer at Tel Aviv University, Faculty of Engineering, and a technology consultant to defense industries. He has thirty years of experience in the development and production of advanced land combat systems and is a recipient of the Israel Defense Award. His research at the LAMBDA laboratory focuses on Multi-Agent Reinforcement Learning and autonomous AI applications.
Evgeny Kagan, PhD, CandSc-Eng, is a Senior Lecturer at the Department of Industrial Engineering, Ariel University, and a Research Fellow at LAMBDA laboratory. With over thirty years of experience in applied mathematics and engineering, he has authored more than eighty scientific publications including four books.
Irad Ben-Gal, PhD, is a Full Professor at the Faculty of Engineering and a head of LAMBDA laboratory at Tel Aviv University and a world-renowned expert in data science, AI, and machine learning with over twenty-five years of academic and practical experience. He co-heads the TAU/Stanford University Digital Living 2030 initiative, has published four books, more than 150 scientific papers and patents, and has collaborated with Oracle, Intel, GM, AT&T, Applied Materials, Siemens, Kimberly Clark and Nokia.
Evgeny Kagan, PhD, CandSc-Eng, is a Senior Lecturer at the Department of Industrial Engineering, Ariel University, and a Research Fellow at LAMBDA laboratory. With over thirty years of experience in applied mathematics and engineering, he has authored more than eighty scientific publications including four books.
Irad Ben-Gal, PhD, is a Full Professor at the Faculty of Engineering and a head of LAMBDA laboratory at Tel Aviv University and a world-renowned expert in data science, AI, and machine learning with over twenty-five years of academic and practical experience. He co-heads the TAU/Stanford University Digital Living 2030 initiative, has published four books, more than 150 scientific papers and patents, and has collaborated with Oracle, Intel, GM, AT&T, Applied Materials, Siemens, Kimberly Clark and Nokia.
Cuprins
Preface ix
About the Companion Website xi
1 Introduction 1
1.1 The Problem of Search Under Uncertainty 1
1.2 Book Objectives and Main Contributions 2
1.3 Structure of the Book 3
2 Background 5
2.1 Probabilistic Search for Static and Moving Targets 6
2.2 Search in Shadowed Space 10
2.3 Search with False-Positive and False-Negative Errors 16
2.4 Conclusions 19
3 Problem Formulation and Basic Search Procedure 21
3.1 Basic Assumptions 21
3.2 Sensor's Fusion and Dynamic Probability Map 24
3.3 Search Policy and Sensing 26
3.3.1 Search Policy 26
3.3.2 Search Time and Sensors' Sensitivity 27
3.3.3 Numerical Simulations 28
3.4 Conclusions 30
4 Reactive Detection Algorithms 31
4.1 Agents' Policies and Decision-making 31
4.2 Policies Control and Brute-Force Learning 34
4.3 Numerical Simulations 35
4.3.1 Detection by a Single Agent 36
4.3.2 Detection by Multiple Agents 38
4.4 Conclusions 42
5 Search with Learning 43
5.1 Reinforcement Learning 43
5.2 Search with Supervised and Unsupervised Learning 47
5.3 Two-Actor Search 50
5.3.1 Pursuit-Evasion Game 51
5.3.2 Actor-Critic Approach 52
5.4 Conclusions 53
6 Search with Deep Q-Learning: Single Agent 55
6.1 Problem Formulation and Notation 55
6.2 Decision-making Policy and Deep Q-Learning Solution 58
6.2.1 Agent's Actions and Decision-making 58
6.2.2 Dynamic Programming and Target Neural Networks 59
6.2.3 Model-Free and Model-Based Learning 60
6.2.4 Choice of the Actions at the Learning Stage 63
6.2.5 The Q-max Algorithm 63
6.2.6 The SPL Algorithm 67
6.3 Numerical Simulations 67
6.3.1 Network Training in the Q-max Algorithm 68
6.3.2 Detection by the Q-max and SPL Algorithms 69
6.3.3 Comparison Between Q-max and SPL Algorithms and One-Step Heuristics 69
6.3.4 Comparison Between SPL Algorithm and Optimal Solution 73
6.3.5 Run Times and Mean-Squared Errors for Different Sizes of Data Sets 75
6.4 Conclusions 76
7 Search with Deep Q-learning: Multiple Agents 77
7.1 Problem Formulation and Notation 77
7.2 Cooperative Detection Using Voronoi Regions and Deep Q-Learning 78
7.2.1 Agents' Actions and Decision-making 79
7.2.2 Reactive Decision-making in Voronoi Regions: A Distributed EIG Algorithm 79
7.2.3 Collective Deep Q-Learning Approach 81
7.3 Numerical Simulations 88
7.3.1 Detection of Static Targets 89
7.3.2 Detection of Moving Targets 92
7.3.3 Learning Errors and Run Time of the Collective Q-max Algorithm 94
7.4 Conclusions 95
8 Conclusions 97
References 99
Index 103
About the Companion Website xi
1 Introduction 1
1.1 The Problem of Search Under Uncertainty 1
1.2 Book Objectives and Main Contributions 2
1.3 Structure of the Book 3
2 Background 5
2.1 Probabilistic Search for Static and Moving Targets 6
2.2 Search in Shadowed Space 10
2.3 Search with False-Positive and False-Negative Errors 16
2.4 Conclusions 19
3 Problem Formulation and Basic Search Procedure 21
3.1 Basic Assumptions 21
3.2 Sensor's Fusion and Dynamic Probability Map 24
3.3 Search Policy and Sensing 26
3.3.1 Search Policy 26
3.3.2 Search Time and Sensors' Sensitivity 27
3.3.3 Numerical Simulations 28
3.4 Conclusions 30
4 Reactive Detection Algorithms 31
4.1 Agents' Policies and Decision-making 31
4.2 Policies Control and Brute-Force Learning 34
4.3 Numerical Simulations 35
4.3.1 Detection by a Single Agent 36
4.3.2 Detection by Multiple Agents 38
4.4 Conclusions 42
5 Search with Learning 43
5.1 Reinforcement Learning 43
5.2 Search with Supervised and Unsupervised Learning 47
5.3 Two-Actor Search 50
5.3.1 Pursuit-Evasion Game 51
5.3.2 Actor-Critic Approach 52
5.4 Conclusions 53
6 Search with Deep Q-Learning: Single Agent 55
6.1 Problem Formulation and Notation 55
6.2 Decision-making Policy and Deep Q-Learning Solution 58
6.2.1 Agent's Actions and Decision-making 58
6.2.2 Dynamic Programming and Target Neural Networks 59
6.2.3 Model-Free and Model-Based Learning 60
6.2.4 Choice of the Actions at the Learning Stage 63
6.2.5 The Q-max Algorithm 63
6.2.6 The SPL Algorithm 67
6.3 Numerical Simulations 67
6.3.1 Network Training in the Q-max Algorithm 68
6.3.2 Detection by the Q-max and SPL Algorithms 69
6.3.3 Comparison Between Q-max and SPL Algorithms and One-Step Heuristics 69
6.3.4 Comparison Between SPL Algorithm and Optimal Solution 73
6.3.5 Run Times and Mean-Squared Errors for Different Sizes of Data Sets 75
6.4 Conclusions 76
7 Search with Deep Q-learning: Multiple Agents 77
7.1 Problem Formulation and Notation 77
7.2 Cooperative Detection Using Voronoi Regions and Deep Q-Learning 78
7.2.1 Agents' Actions and Decision-making 79
7.2.2 Reactive Decision-making in Voronoi Regions: A Distributed EIG Algorithm 79
7.2.3 Collective Deep Q-Learning Approach 81
7.3 Numerical Simulations 88
7.3.1 Detection of Static Targets 89
7.3.2 Detection of Moving Targets 92
7.3.3 Learning Errors and Run Time of the Collective Q-max Algorithm 94
7.4 Conclusions 95
8 Conclusions 97
References 99
Index 103