AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Distinguishing tests for nondeterministic and probabilistic machines
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Journal of Artificial Intelligence Research
Fault-model-based test generation for embedded software
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A new d-DNNF-based bound computation algorithm for functional E-MAJSAT
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
sKizzo: a suite to evaluate and certify QBFs
CADE' 20 Proceedings of the 20th international conference on Automated Deduction
An Integrated Diagnostic Development Process for Automotive Engine Control Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The goal of testing is to distinguish between a number of hypotheses about a system--for example, different diagnoses of faults-- by applying input patterns and verifying or falsifying the hypotheses from the observed outputs. Optimal distinguishing tests (ODTs) are those input patterns that are most likely to distinguish between hypotheses about non-deterministic systems. Finding ODTs is practically important, but it amounts in general to determining a ratio of model counts and is therefore computationally very expensive. In this paper, we present a novel approach to constraint-based ODT generation, which uses structural properties of the system to limit the complexity of computation.We first construct a compact graphical representation of the testing problem via compilation into decomposable negation normal form. Based on this compiled representation, we show how one can evaluate distinguishing tests in linear time, which allows us to efficiently determine an ODT. Experimental results from a real-world application show that our method can compute ODTs for instances that were intractable for previous approaches.