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Probably Approximately Optimal Satisficing Strategies (1990)
| Content Provider | CiteSeerX |
|---|---|
| Author | Greiner, Russell Orponen, Pekka |
| Abstract | A satisficing search problem consists of a set of probabilistic experiments to be performed in some order, seeking a satisfying configuration of successes and failures. The expected cost of the search depends both on the success probabilities of the individual experiments, and on the search strategy , which specifies the order in which the experiments are to be performed. A strategy that minimizes the expected cost is optimal. Earlier work has provided "optimizing functions" that compute optimal strategies for certain classes of search problems from the success probabilities of the individual experiments. We extend those results by providing a general model of such strategies, and an algorithm pao that identifies an approximately optimal strategy when the probability values are not known. The algorithm first estimates the relevant probabilities from a number of trials of each undetermined experiment, and then uses these estimates, and the proper optimizing function, to identify a stra... |
| File Format | |
| Journal | ARTIFICIAL INTELLIGENCE |
| Journal | Artificial Intelligence |
| Publisher Date | 1990-01-01 |
| Access Restriction | Open |
| Subject Keyword | Compute Optimal Strategy Search Problem Algorithm Pao Optimal Strategy Individual Experiment Satisfying Configuration Certain Class Undetermined Experiment Relevant Probability Expected Cost Search Strategy Probability Value Optimal Satisficing Strategy General Model Proper Optimizing Function Satisficing Search Problem Probabilistic Experiment Success Probability |
| Content Type | Text |
