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Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems (Hardcover, 1st ed. 2017)
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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems (Hardcover, 1st ed. 2017)
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This book presents new efficient methods for optimization in
realistic large-scale, multi-agent systems. These methods do not
require the agents to have the full information about the system,
but instead allow them to make their local decisions based only on
the local information, possibly obtained during communication with
their local neighbors. The book, primarily aimed at researchers in
optimization and control, considers three different information
settings in multi-agent systems: oracle-based, communication-based,
and payoff-based. For each of these information types, an efficient
optimization algorithm is developed, which leads the system to an
optimal state. The optimization problems are set without such
restrictive assumptions as convexity of the objective functions,
complicated communication topologies, closed-form expressions for
costs and utilities, and finiteness of the system's state space.
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