IRIS publication 70046631
A Model-Based Active Testing Approach to Sequential Diagnosis
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TY - JOUR - Feldman, A,Provan, G,van Gemund, A - 2010 - January - Journal of Artificial Intelligence Research - A Model-Based Active Testing Approach to Sequential Diagnosis - Validated - () - FAULT-DIAGNOSIS ALGORITHMS - 39 - 301 - 334 - 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 Fractal(G). We compare the decrease in the number of remaining minimal cardinality diagnoses of Fractal(G) to that of two more Fractal algorithms: Fractal(ATPG) and Fractal(P). Fractal(ATPG) is based on ATPG and sequential diagnosis while Fractal(P) 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. DA - 2010/01 ER -
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@article{V70046631, = {Feldman, A and Provan, G and van Gemund, A }, = {2010}, = {January}, = {Journal of Artificial Intelligence Research}, = {A Model-Based Active Testing Approach to Sequential Diagnosis}, = {Validated}, = {()}, = {FAULT-DIAGNOSIS ALGORITHMS}, = {39}, pages = {301--334}, = {{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 Fractal(G). We compare the decrease in the number of remaining minimal cardinality diagnoses of Fractal(G) to that of two more Fractal algorithms: Fractal(ATPG) and Fractal(P). Fractal(ATPG) is based on ATPG and sequential diagnosis while Fractal(P) 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.}}, source = {IRIS} }
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AUTHORS | Feldman, A,Provan, G,van Gemund, A | ||
YEAR | 2010 | ||
MONTH | January | ||
JOURNAL_CODE | Journal of Artificial Intelligence Research | ||
TITLE | A Model-Based Active Testing Approach to Sequential Diagnosis | ||
STATUS | Validated | ||
TIMES_CITED | () | ||
SEARCH_KEYWORD | FAULT-DIAGNOSIS ALGORITHMS | ||
VOLUME | 39 | ||
ISSUE | |||
START_PAGE | 301 | ||
END_PAGE | 334 | ||
ABSTRACT | 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 Fractal(G). We compare the decrease in the number of remaining minimal cardinality diagnoses of Fractal(G) to that of two more Fractal algorithms: Fractal(ATPG) and Fractal(P). Fractal(ATPG) is based on ATPG and sequential diagnosis while Fractal(P) 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. | ||
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