Heuristics for Two Extensions of Basic Troubleshooting
SCAI '01 Proceedings of the Seventh Scandinavian Conference on Artificial Intelligence
Troubleshooting with Simultaneous Models
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
The SACSO methodology for troubleshooting complex systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Troubleshooting when Action Costs are Dependent with Application to a Truck Engine
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
| Hi-index | 12.05 |
Troubleshooting is about computing a cost-efficient way to repair a faulty device. In this paper we investigate the most simple action-based troubleshooting scenario with a single extension: the overall system test is not required to be performed after each action, but it may be postponed until several actions have been performed. As shown by Kadane and Simon, the simple troubleshooting scenario is easily solvable in polynomial time. However, we conjecture that the new troubleshooting scenario is NP-hard and therefore describe an @Q(n^3) time heuristic. The new heuristic guarantees optimality for a class of models, and for other classes of models we benchmark it against the optimal solution and other simpler greedy heuristics. The benchmark is performed on artificial models as well as a real-world troubleshooting model, and it suggests that the heuristics are close to optimal.