Artificial Intelligence
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
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Using Model Counting to Find Optimal Distinguishing Tests
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Near-optimal observation selection using submodular functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Fault-model-based test generation for embedded software
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Hierarchical diagnosis of multiple faults
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Active probing strategies for problem diagnosis in distributed systems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Optimal and near-optimal test sequencing algorithms with realistic test models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Rollout strategies for sequential fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Bottom-Up Construction of Minimum-Cost and/ or Trees for Sequential Fault Diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Combinational test generation using satisfiability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A model-based active testing approach to sequential diagnosis
Journal of Artificial Intelligence Research
Sequential diagnosis by abstraction
Journal of Artificial Intelligence Research
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Model-Based Diagnosis (MBD) approaches often yield a large number of diagnoses, severely limiting their practical utility. This paper presents a novel active testing approach based on MBD techniques, called FRACTAL (FRamework for ACtive Testing ALgorithms), which, given a system description, computes a sequence of control settings for reducing the number of diagnoses. The approach complements probing, sequential diagnosis, and ATPG, and applies to systems where additional tests are restricted to setting a subset of the existing system inputs while observing the existing outputs. This paper evaluates the optimality of FRACTAL, both theoretically and empirically. FRACTAL generates test vectors using a greedy, next-best strategy and a low-cost approximation of diagnostic information entropy. Further, the approximate sequence computed by FRACTAL's greedy approach is optimal over all poly-time approximation algorithms, a fact which we confirm empirically. Extensive experimentation with ISCAS85 combinational circuits shows that FRACTAL reduces the number of remaining diagnoses according to a steep geometric decay function, even when only a fraction of inputs are available for active testing.