Name
Title | ||
|---|---|---|
Modeling Intelligent System Execution as State Transition Diagrams to Support Debugging |
Abstract | ||
|---|---|---|
Currently, few tools are available for assisting developers with debugging intelligent systems. Because these systems rely heavily on context dependent knowledge and sometimes stochas- tic decision making, replicating problematic performance may be dicult. Consequently, we adopt a statistical approach to modeling behavior as the basis for identifying poten- tial 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. |
Year | Venue | Keywords |
|---|---|---|
1997 | AADEBUG | state transition diagram,context dependent,neural network,state transition |
Field | DocType | Citations |
Publication,Software engineering,Drag and drop,Computer science,Theoretical computer science,Neural network controller,Electronic publishing,Debugging | Conference | 1 |
PageRank | References | Authors |
0.57 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adele E. Howe | 1 | 561 | 65.70 |
| Gabriel Somlo | 2 | 95 | 6.95 |