Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Discrete Applied Mathematics - Special issue on the satisfiability problem and Boolean functions
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Perception, attention, and resources: a decision-theoretic approach to graphics rendering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
TestAnt: An ant colony system approach to sequential testing under precedence constraints
Expert Systems with Applications: An International Journal
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We consider scenarios in which a sequence of tests is to be applied to an object; the result of a test may be that a decision (such as the classification of the object) can be made without running additional tests. Thus, one seeks an ordering of the tests that is optimal in some sense, such as minimum expected resource consumption. Sequences of tests are commonly used in computer vision (Paul A. Viola and Michael J. Jones (2001) [15]) and other applications. Finding an optimal ordering is easy when the tests are completely independent. Introducing precedence constraints, we show that the optimization problem becomes NP-hard when the constraints are given by means of a general partial order. Restrictions of the constraints to non-trivial special cases that allow for low-order polynomial-time algorithms are examined.