Abstract
A computational model of persistent beliefs is developed supporting both the temporal information and the nested belief model. The model has explicit lower and upper time-bounds when representing facts and beliefs. This allows nonambiguous interpretation of persistent beliefs. It has a consistency-checking algorithm between two beliefs. The model can incorporate a new set of beliefs into its old beliefs by using its belief-revision algorithm.
| Original language | English |
|---|---|
| Number of pages | 1 |
| Publication status | Published - 1996 Dec 1 |
| Event | Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) - Portland, OR, USA Duration: 1996 Aug 4 → 1996 Aug 8 |
Conference
| Conference | Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) |
|---|---|
| City | Portland, OR, USA |
| Period | 96/8/4 → 96/8/8 |
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
Cite this
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Computational model of persistent beliefs. / Park, Sunju.
1996. Paper presented at Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2), Portland, OR, USA, .Research output: Contribution to conference › Paper
TY - CONF
T1 - Computational model of persistent beliefs
AU - Park, Sunju
PY - 1996/12/1
Y1 - 1996/12/1
N2 - A computational model of persistent beliefs is developed supporting both the temporal information and the nested belief model. The model has explicit lower and upper time-bounds when representing facts and beliefs. This allows nonambiguous interpretation of persistent beliefs. It has a consistency-checking algorithm between two beliefs. The model can incorporate a new set of beliefs into its old beliefs by using its belief-revision algorithm.
AB - A computational model of persistent beliefs is developed supporting both the temporal information and the nested belief model. The model has explicit lower and upper time-bounds when representing facts and beliefs. This allows nonambiguous interpretation of persistent beliefs. It has a consistency-checking algorithm between two beliefs. The model can incorporate a new set of beliefs into its old beliefs by using its belief-revision algorithm.
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M3 - Paper
AN - SCOPUS:0030364052
ER -