Artificial Intelligence
Artificial Intelligence
One step lookahead is pretty good
Readings in model-based diagnosis
Building problem solvers
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
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
Accuracy vs. efficiency trade-offs in probabilistic diagnosis
Eighteenth national conference on Artificial intelligence
Polynomially Complete Fault Detection Problems
IEEE Transactions on Computers
Computing observation vectors for max-fault min-cardinality diagnoses
AAAI'08 Proceedings of the 23rd 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
FRACTAL: efficient fault isolation using active testing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Solving strong-fault diagnostic models by model relaxation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Optimal and near-optimal test sequencing algorithms with realistic test models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Sequential testing algorithms for multiple fault diagnosis
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
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Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called Fractal (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in Fractal) in terms of a greedy approximation algorithm called FractalG. We compare the decrease in the number of remaining minimal cardinality diagnoses of FractalG to that of two more Fractal algorithms: FractalATPG and FractalP. FractalATPG is based on ATPG and sequential diagnosis while FractalP is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the Fractal algorithms. We empirically evaluate the trade-offs of the three Fractal algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.