Issues in computational Vickrey auctions
International Journal of Electronic Commerce - Special issue: Intelligent agents for electronic commerce
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This paper presents a method for automatically deriving a time- and content-dependent information value function for probabilistic information. This function describes analytically what real world value an agent can obtain by using a certain piece of information at a certain time. The general form of this function is formulated and a specific example with two-valued outputs is presented in the factory scheduling domain. The information value function forms a formal basis for decision-theoretic deliberation control, because the control decisions can be made in order to maximize a value directly derived from the agent''s situation in its environment. We show how an expert agent can use another agent''s communicated information value function to allocate the right amount of time to an anytime algorithm whose results the other agent will use. The particular anytime algorithm is generated from an incomplete algorithm by using prior execution statistics. This method enables a rational use of incomplete algorithms that are often effective, but suffer from not halting on every input. As an example, we present an anytime algorithm for determining 3-satisfiability (3SAT). The algorithm approximates 3SAT probabilistically by refining a satisfiability probability estimate over time. To enhance accuracy, both the initial satisfiability estimate and the performance profile of the anytime algorithm are parameterized by problem instance features. The result of each execution step of the algorithm is used to dynamically predict the results of future execution steps.