Communications of the ACM
Statistics: principles and methods
Statistics: principles and methods
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Improving learning performance through rational resource allocation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Composer: A decision-theoretic approach to adaptive problem solving
Composer: A decision-theoretic approach to adaptive problem solving
Learning Coordination Strategies for Cooperative Multiagent Systems
Machine Learning
Improvement of HITS-based algorithms on web documents
Proceedings of the 11th international conference on World Wide Web
ECAI '00 Proceedings of the Workshop on Local Search for Planning and Scheduling-Revised Papers
Adaptive problem-solving for large-scale scheduling problems: a case study
Journal of Artificial Intelligence Research
Efficient heuristic hypothesis ranking
Journal of Artificial Intelligence Research
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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.