Communications of the ACM
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Artificial Intelligence
Learning in embedded systems
Improving learning performance through rational resource allocation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On efficient heuristic ranking of hypotheses
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
On the Efficient Allocation of Resources for Hypothesis Evaluation: A Statistical Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Composer: A decision-theoretic approach to adaptive problem solving
Composer: A decision-theoretic approach to adaptive problem solving
A new model for inductive inference
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
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
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This paper considers the problem of learning the ranking of a set of stochastic alternatives based upon incomplete information (i.e., a limited number of samples). We describe a system that, at each decision cycle, outputs either a complete ordering on the hypotheses or decides to gather additional information (i.e., observations) at some cost. The ranking problem is a generalization of the previously studied hypothesis selection problem -- in selection, an algorithm must select the single best hypothesis, while in ranking, an algorithm must order all the hypotheses. The central problem we address is achieving the desired ranking quality while minimizing the cost of acquiring additional samples. We describe two algorithms for hypothesis ranking and their application for the probably approximately correct (PAC) and expected loss (EL) learning criteria. Empirical results are provided to demonstrate the effectiveness of these ranking procedures on both synthetic and real-world datasets.