Name
Abstract | ||
|---|---|---|
Learning how to make decisions in a domain is a critical aspect of intelligent planning behavior. The ability of a planner to adapt its decision-making to a domain depends in part upon its ability to optimize the tradeoff between the sophistication of its decision procedures and their cost. Since it is difficult to optimize this tradeoff on a priori grounds alone, we propose that a planner start with a relatively simple set of decision procedures, and add complexity in response to experience gained in the application of its decision-making to real-world problems. Our model of this adaptation process is based on the explanation of failures, in that it is the analysis of bad decisions that drives the improvement of the decision procedures. We have developed a test-bed system for the implementation of planning models employing such an approach, and have demonstrated the ability of such a model to improve its procedure for projecting the effects of its moves in chess. |
Year | Venue | Keywords |
|---|---|---|
1989 | IJCAI | bad decision,intelligent planning behavior,critical aspect,decision procedure,adaptive model,test-bed system,planner start,adaptation process,simple set |
Field | DocType | Citations |
Computer science,A priori and a posteriori,Planner,Simple set,Intelligent planning,Artificial intelligence,Sophistication,Machine learning | Conference | 8 |
PageRank | References | Authors |
1.18 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gregg Collins | 1 | 192 | 29.73 |
| Lawrence Birnbaunv | 2 | 8 | 1.18 |
| Bruce Krulwiclv | 3 | 8 | 1.18 |