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| Files: | Description | File size | Format | |
| Fulltext | 489K | PDF (requires Acrobat Reader) | ||
| Fulltext | 492K | PostScript (requires a PostScript Reader) | ||
| Authors: | Adele E. Howe and Gabriel Somlo | |||
| Article title: | Modeling Intelligent System Execution as State Transition Diagrams to Support Debugging | |||
| Conference: | AADEBUG'97. Proceedings of the Third International Workshop on Automatic Debugging: Linköping, Sweden, May 26-27, 1997 | |||
| Publ. type: | Article | |||
| Article No: | 7 | |||
| Language: | English | |||
| Abstract [en]: | Currently, few tools are available for assisting
developers with debugging intelligent systems. Because these systems
rely heavily on context dependent knowledge and sometimes stochastic
decision making, replicating problematic performance may be difficult.
Consequently, we adopt a statistical approach to modeling behavior as
the basis for identifying potential causes of failure. This paper
describes an algorithm for constructing state transition models of
system behavior from execution traces. The algorithm is the latest in
a family of statistics based algorithms for modelling system execution
called Dependency Detection. We present preliminary accuracy results
for the algorithm on synthetically generated data and an example of
its use in debugging a neural network controller for a race car
simulator.
This research was supported in part by by NSF Career Award IRI-9624058 and by DARPA-AFOSR contract F30602-93-C-0100 and F30602-95-0257. Wa also wish to thank Larry Pyeatt for his contributions on the previous version of the algorithm and for collecting the RARS data. |
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| Publisher: | Linköping University Electronic Press | |||
| Year: | 1997 | |||
| Available: | 1997-09-10 | |||
| No. of pages: | 8 | |||
| Pages: | 79-86 | |||
| Series: | Linköping Electronic Articles in Computer and Information Science | |||
| ISSN: | 1401-9841 | |||
| Volume: | 2 | |||
| No: | 009 | |||
| Series: | Linköping Electronic Conference Proceedings | |||
| ISSN (print): | 1650-3686 | |||
| ISSN (online): | 1650-3740 | |||
| Issue: | 1 | |||
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