Artificial Intelligence
A theory of diagnosis from first principles
Readings in model-based diagnosis
Remote Agent: to boldly go where no AI system has gone before
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Debugging sequential circuits using Boolean satisfiability
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
A new bayesian approach to multiple intermittent fault diagnosis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Integrated plan tracking and prognosis for autonomous production processes
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Constraint-based integration of plan tracking and prognosis for autonomous production
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Automated plan assessment in cognitive manufacturing
Advanced Engineering Informatics
Diagnosing multiple intermittent failures using maximum likelihood estimation
Artificial Intelligence
Plan assessment for autonomous manufacturing as Bayesian inference
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
In model-based control, a planner uses a system description to create a plan that achieves production goals (Fikes & Nilsson 1971). The same description can be used by model-based diagnosis to infer the condition of components in a system from partially informative sensors. Prior work has demonstrated that diagnosis can be used to adapt the control of a system to changes in its components. However diagnosis must either make inferences from passive observations of production, or production must be halted to take diagnostic actions. We observe that the declarative nature of model-based control allows the planner to achieve production goals in multiple ways. This exibility can be exploited with a novel paradigm we call pervasive diagnosis which produces diagnostic production plans that simultaneously achieve production goals while uncovering additional information about component health. We present an efficient heuristic search for these diagnostic production plans and show through experiments on a model of an industrial digital printing press that the theoretical increase in information can be realized on practical real-time systems. We obtain higher long-run productivity than a decoupled combination of planning and diagnosis.