A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Neural computing: an introduction
Neural computing: an introduction
Characterizing diagnoses and systems
Artificial Intelligence
Building problem solvers
ModGen: theorem proving by model generation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Introduction to knowledge systems
Introduction to knowledge systems
Test Pattern Generation for Realistic Bridge Faults in CMOS ICs
Proceedings of the IEEE International Test Conference on Test: Faster, Better, Sooner
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Programming in Propositional Logic or Reductions: Back to the Roots (Satisfiability)
Programming in Propositional Logic or Reductions: Back to the Roots (Satisfiability)
| Hi-index | 0.00 |
Model-based diagnosis of technical systems requires both a simulation machinery and a logic calculus. The former is responsible for the system's behavior analysis, the latter controls the actual diagnosis process. Especially when pursuing qualitative simulation, it makes sense to realize the simulation machinery with a logic calculus as well. Say, a qualitatively described hypothesis can directly be mapped onto an instance of the well-known SAT problem. Likewise, an entire diagnosis process, i. e., a sequence of hypothesis refinements, represents a set of SAT problems.This paper reports on the operationalization of such a SAT-based diagnosis approach. A specific characteristic here is the idea to exploit an ordering of the logical formulas according to their likeliness of being satisfiable. This idea is new in the context of qualitative reasoning, and it leads to a considerable speed up of the diagnosis process. Its applicability has been evaluated in the domain of hydraulic circuit diagnosis.