| dc.contributor | 資科系 | |
| dc.creator (作者) | Liu, Chao-lin;Wellman, Michael P. | |
| dc.creator (作者) | 劉昭麟 | zh_TW |
| dc.date (日期) | 1994 | |
| dc.date.accessioned | 17-Jun-2015 15:07:34 (UTC+8) | - |
| dc.date.available | 17-Jun-2015 15:07:34 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Jun-2015 15:07:34 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75883 | - |
| dc.description.abstract (摘要) | One important factor determining the computa - tional complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an any- time procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the proce - dure exhibits a smooth improvement in approxi - mation quality as computation time increases. This suggests that state-space abstraction is one more useful control parameter for designing real- time probabilistic reasoners. | |
| dc.format.extent | 194 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Uncertainty in Artificial Intelligence - UAI , pp. 567-574 | |
| dc.title (題名) | State-Space Abstraction for Anytime Evaluation of Probabilistic Networks | |
| dc.type (資料類型) | article | en |
