Multi-Agent Search under Uncertainty: Reactive and Deep Q-Learning Methods - Hardcover

Über die Autorin bzw. den Autor

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.

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Plan optimal multi-robot search paths despite imperfect sensor information

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

Designed for graduate students and researchers in robotics and reinforcement learning, this book bridges advanced theory with practical application. Professional developers building autonomous systems will find algorithms tested in real-world robotic development.