Name
Abstract | ||
|---|---|---|
Automated problem-solving for engineered devices is based on models that capture the essential aspects of the behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal of task-dependent qualitative domain abstraction is to determine maximal partitions for the variables' domains (termed qualitative values) that retain all the necessary distinctions. We present a formalization of this problem within a relational (constraint-based) framework, and devise solutions to automatically determine qualitative values for a device model. The results enhance the ability to use a behavior model of a device as a common basis to support different tasks along its life cycle. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.artint.2004.01.005 | Artif. Intell. |
Keywords | Field | DocType |
behavior model,device model,task-dependent qualitative domain abstraction,abstracting behavior model,possible observation,specified granularity,diagnostic task,different task,domain abstraction,qualitative reasoning,qualitative value,engineered device,model-based systems,life cycle,behavior modeling | Abstraction,Model-based reasoning,Artificial intelligence,Granularity,Mathematics,Qualitative reasoning | Journal |
Volume | Issue | ISSN |
162 | 1-2 | 0004-3702 |
Citations | PageRank | References |
11 | 0.68 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Sachenbacher | 1 | 11 | 0.68 |
| P. Struss | 2 | 26 | 2.60 |