Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers.
Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks.
The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Levy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker–Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.
Eugene Kagan is a senior lecturer in the Department of Industrial Engineering at Ariel University and an advisor in the Department of Mathematics at the Weizmann Institute of Science. His research interests include dynamical systems theory, applied probability, and robotics.
Irad Ben-Gal is an associate professor and a chair of the Department of Industrial Engineering at Tel Aviv University. His research interests include applied probability, machine learning, and information theory applications to industrial and service systems.
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