A Model-Based Active Testing Approach to Sequential Diagnosis

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.

On Active Learning Strategies for Sequential Diagnosis

“…In SD the set V is often referred to as leading diagnoses [10] and usually comprises the most probable [7] or minimumcardinality [12] hypotheses. As with H, a query Q partitions V into V…”

“…However, usually such diagnosis methods have to deal with a large number of different fault hypotheses. To provide for hypotheses discrimination, Sequential Diagnosis (SD) techniques [9,29,12,39,45] gather additional information in terms of observations or tests. The goal in SD is the minimization of the effort or cost until complete (or at least a reasonable) diagnostic accuracy is achieved.…”

“…Unfortunately, this problem has been shown to be NP-complete [18,27]. Thus, as a tradeoff between optimality and computational complexity, it is current practice in SD to rely on myopic methods to guide hypotheses discrimination [9,12,16,39,35]. Empirical (e.g.…”

Reducing Model-Based Diagnosis to Knowledge Base Debugging

“…Since then it has generated considerable interest in a great variety of application domains such as physical devices [2,14,3], software [22,12], configuration systems [6], recommender systems [5] and knowledge bases [9,10,20,16]. This has led to a number of different related diagnosis paradigms that build upon MBD and extend, generalize or specialize MBD.…”

Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

“…In case the available observations about the system are insufficient for successful fault localization, i.e., multiple diagnoses exist, sequential diagnosis (SQD) methods collect additional information by generating a sequence of queries [7,26,10,37,35]. 1 If chosen properly, each query's answer eliminates some diagnoses and thus reduces the diagnostic uncertainty.…”