Efficient Model-Based Diagnosis of Sequential Circuits

2020-06-02

Proceedings of the AAAI Conference on Artificial Intelligence, 34

Volume

Vol. 34 No. 03: AAAI-20 Technical Tracks 3

AAAI Technical Track: Knowledge Representation and Reasoning

In Model-Based Diagnosis (MBD), we concern ourselves with the health and safety of physical and software systems. Although we often use different knowledge representations and algorithms, some tools like satisfiability (SAT) solvers and temporal logics, are used in both domains. In this paper we introduce Finite Trace Next Logic (FTNL) models of sequential circuits and propose an enhanced algorithm for computing minimal-cardinality diagnoses. Existing state-of-the-art satisfiability algorithms for minimal diagnosis use Sorting Networks (SNs) for constraining the cardinality of the diagnostic candidates. In our approach we exploit Multi-Operand Adders (MOAs). Based on extensive tests with ISCAS-89 circuits, we found that MOAs enable Conjunctive Normal Form (CNF) encodings that are significantly more compact. These encodings lead to 19.7 to 67.6 times fewer variables and 18.4 to 62 times fewer clauses. For converting an FTNL model to CNF, we could achieve a speed-up ranging from 6.2 to 22.2. Using SNs fosters 3.4 to 5.5 times faster on-line satisfiability checking though. This makes MOAs preferable for applications where RAM and off-line time are more limited than on-line CPU time.

10.1609/aaai.v34i03.5670