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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.

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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Last updated: 2017-02-21