This
book mainly aims at solving the problems in both cooperative and competitive
multi-agent systems (MASs), exploring aspects such as how agents can
effectively learn to achieve the shared optimal solution based on their local
information and how they can learn to increase their individual utility by
exploiting the weakness of their opponents. The book describes fundamental and
advanced techniques of how multi-agent systems can be engineered towards the
goal of ensuring fairness, social optimality, and individual rationality; a
wide range of further relevant topics are also covered both theoretically and
experimentally. The book will be beneficial to researchers in the fields of
multi-agent systems, game theory and artificial intelligence in general, as well
as practitioners developing practical multi-agent systems.