Name
Abstract | ||
|---|---|---|
Recently we showed that under very reasonable conditions, incomplete, real-time search methods like RTA* work better with pessimistic heuristic functions than with optimistic, admissible heuristic functions of equal quality. The use of pessimistic heuristic function,; results in higher percentage of correct decisions and in shorter solution lengths. We extend this result to learning RTA* (LRTA*) and demonstrate that the use of pessimistic instead of optimistic (or mixed) heuristic functions of equal quality results in much faster learning process at the cost of just marginally worse quality of converged solutions. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.3233/978-1-58603-891-5-897 | Frontiers in Artificial Intelligence and Applications |
DocType | Volume | ISSN |
Conference | 178 | 0922-6389 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksander Sadikov | 1 | 53 | 9.96 |
| Ivan Bratko | 2 | 1526 | 405.03 |