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Issue No.07 - July (1995 vol.17)
pp: 652-665
<p><it>Abstract</it>—This paper considers the decision-making problem of selecting a strategy from a set of alternatives on the basis of incomplete information (e.g., a finite number of observations). At any time the system can adopt a particular strategy or decide to gather additional information at some cost. Balancing the expected utility of the new information against the cost of acquiring the information is the central problem we address. In our approach, the cost and utility of applying a particular strategy to a given problem are represented as random variables from a parametric distribution. By observing the performance of each strategy on a randomly selected sample of problems, we can use parameter estimation techniques to infer statistical models of performance on the general population of problems. These models can then be used to estimate: 1) the utility and cost of acquiring additional information and 2) the desirability of selecting a particular strategy from a set of choices. Empirical results are presented that demonstrate the effectiveness of the hypothesis evaluation techniques for tuning system parameters in a NASA antenna scheduling application.</p>
Machine learning, the utility problem, planning and scheduling, parameter estimation, adaptive problem-solving.
Steve Chien, Jonathan Gratch, Michael Burl, "On the Efficient Allocation of Resources for Hypothesis Evaluation: A Statistical Approach", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.17, no. 7, pp. 652-665, July 1995, doi:10.1109/34.391408
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