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Adaptation and evolution in dynamic persistent environments. (English) Zbl 1276.68072
Goldin, Dina (ed.) et al., Proceedings of the workshop on the foundations of interactive computation (FInCo 2005), Edinburgh, UK, April 9, 2005. Amsterdam: Elsevier. Electronic Notes in Theoretical Computer Science 141, No. 5, 163-179 (2005).
Summary: Optimization (adaptation) of agents interacting with dynamic persistent environments (DPEs) poses a separate class of problems from those of static optimization. Such environments must be incorporated into models of interactive computation.{
}By the No Free Lunch theorem (NFLT), no general-purpose function-optimization algorithm can exist that is superior to random search. But interactive adaptation in environments with persistent state falls outside the scope of the NFLT, and useful general-purpose interactive optimization protocols for DPEs exist, as we show.{
}Persistence of state supports indirect interaction. Based on the observation that mutual causation is inherent to interactive computation, and on the key role of persistent state in multiagent systems, we establish that indirect interaction is essential to multiagent systems (MASs).{
}This work will be useful to researchers in coordination, evolutionary computation, and design of multiagent and adaptive systems.
For the entire collection see [Zbl 1273.68034].
MSC:
68Q05 Models of computation (Turing machines, etc.) (MSC2010)
68Q10 Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.)
68T05 Learning and adaptive systems in artificial intelligence
68T42 Agent technology and artificial intelligence
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