Communications of the ACM
Optimization
Approximate Algorithms for the 0/1 Knapsack Problem
Journal of the ACM (JACM)
Granular computing in data mining
Data mining and computational intelligence
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Some related problems from network flows, game theory and integer programming
SWAT '72 Proceedings of the 13th Annual Symposium on Switching and Automata Theory (swat 1972)
Learning through reinforcement for N-person repeated constrained games
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cognitive Systems Research
A general framework for learning rules from data
IEEE Transactions on Neural Networks
International Journal of Computational Intelligence Studies
A new goodness-of-fit statistical test
Intelligent Decision Technologies
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We deal with a complex game between Alice and Bob where each contender's probability of victory grows monotonically by unknown amounts with the resources employed. For a fixed effort on Alice's part, Bob increases his resources on the basis of the results for each round (victory, tie or defeat) with the aim of reducing the probability of defeat to below a given threshold. We read this goal in terms of computing a confidence interval for the probability of losing and realize that the moves in some contests may bring in an indeterminacy trap: in certain games Bob cannot simultaneously have both a low probability-of-defeat measure and a narrow confidence interval. We use the inferential mechanism called twisting argument to compute the above interval on the basis of two joint statistics. Careful use of such statistics allows us to avoid indeterminacy.