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Extra resources for Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
Thus, for stochasticadaptive plans, U,... (T) = ET. IP. ( T, I). Following this line, a useful performancetarget can be formulated in terms of the greatestpossiblecumulative payoff in the first T time-steps, . U,. €3 .. ,. ~ T)/ UJ(T)] = 1. ~ In other words, the rate at which 'Taccumulatespayoff is, in the limit , the sameas the best possiblerate. Often it is desirableto have a much stronger criterion setting standardson interim behavior. That is, eventhough the payoff rate approaches the optimum, it may take an intolerably long time before it is reasonably close.
4 as an presentsspecialdifficulties. This latter problernis addressed elaborationof the conceptsand techniquesdevelopedin the earlierchapters . : Surnrnarizing Ct, strategiesfor the garne . 4 extendstheseideas). 3, plansfor testingstrategies . Adaptation in Natural and Artificial Systems 8 , the strategic options open to the opponent ; in simple cases, the set of pure . strategies ' " " a x, rankingof plansusingthecumulativepayoff functions , the gambler s ruin criterionbeingan example . 4 . SEARCHES , PATTERN RECOGNITION, AND STATISTICAL INFERENCE Searches occur as the principal element in most problem-solving and goal-attainment attempts, from maze-running through resourceallocation to very complicated planning situations in business, government, and research.
X At = nf - lAi8 Finally , the set d will usually be potential rather than actual. , by rearrangement of components or construction from primitive elements), rather than by selection from an extant set. We will examine all of these possibilities as we go along, noting that relevant elaborations of the elements of d provide a way of specializingthe generalparts of the theory for particular applications. , a trajectory throughd , by making successiveselectionsfrom a set of operatorsO. The particular Adaptation in Natural and ArtificialSystems selectionsmade are influenced by information obtained from the environment E, so that the plan l' typically generatesdifferent trajectories in different environments.
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