Titlebook: Dynamic Pricing and Automated Resource Allocation for Complex Information Services; Reinforcement Learni Michael Schwind Book 2007 Springer

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Dynamic Pricing and Automated Resource Allocation Using Combinatorial Auctions,rent bidding strategies used by proxy-bidding agents that submit bids for purpose of ISIP resource acquisition are evaluated in the context of a utility function that allows the simultaneous valuation of two impact factors by making a trade-off between the amount of resources acquired and the speed at which the resources can be acquired.

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Introduction,act as a control variable in economic processes. The work presented here deals first of all with . for the provision of . in the context of a research project called the . .The research was continued within the project . focusing on the subtopic “dynamic bundle pricing”.

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Michael SchwindIncludes supplementary material:

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Reinforcement Learning for Dynamic Pricing and Automated Resource Allocation,nce. In an RL problem an agent has to learn . behavior by . interaction with a dynamic environment. A crucial advantage of RL compared to other AI approaches like DPS, is the fact that with RL there is no need to specify how the task is to be achieved (Kaelbling et al. 1996).

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Schmelzdiagramme und Legierungen,chnological production processes. The scarcity of resources dominating the optimization of these processes is normally expressed by prices, which can act as a control variable in economic processes. The work presented here deals first of all with . for the provision of . in the context of a research