A Model-Based Active Testing Approach to Sequential Diagnosis

• Published in: The Journal of artificial intelligence research 2010-01, Vol.39, p.301-334
• Main Authors: Feldman, A., Provan, G., Van Gemund, A.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Diagnosis; Diagnostic systems; Experimentation; Fractals; Greedy algorithms; Lower bounds; Model testing; Optimal control; Pattern generation; Test pattern generators
• ISSN: 1076-9757; 1076-9757; 1943-5037
• DOI: 10.1613/jair.3031

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.