Name
Abstract | ||
|---|---|---|
The goal of our research is to construct an integrated model of planning and learning that can account for the acquisition of new planning knowledge. Our approach involves the use of model-based reasoning. In this approach, the system monitors its performance by comparing it with expectations derived from a model of the system's planning architecture. The arguments relating the system's expectations to its underlying model of the planning process are encoded in the form of explicit justification structures. When the system's actual performance diverges from its expectations, it traces back through these justification structures, looking to fault the setting of some controllable parameter of the planner. When such a controllable parameter is isolated, a repair is then effected, in the form of an adjustment to one of these parameters. |
Year | DOI | Venue |
|---|---|---|
1991 | 10.1145/122344.122354 | SIGART Bulletin |
Keywords | Field | DocType |
actual performance diverges,controllable parameter,planning process,planning architecture,model-based reasoning,integrated model,new planning knowledge,justification structure,underlying model,model-based integration,explicit justification structure,model based reasoning | Architecture,Computer science,Planner,Planning process,Artificial intelligence | Journal |
Volume | Issue | Citations |
2 | 4 | 1 |
PageRank | References | Authors |
0.49 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregg Collins | 1 | 192 | 29.73 |
| Lawrence Birnbaum | 2 | 11 | 9.67 |
| Bruce Krulwich | 3 | 238 | 50.70 |
| Michael Freed | 4 | 33 | 6.01 |