Name
Title | ||
|---|---|---|
Probabilistic conceptual network: a belief representation scheme for utility-based categorization |
Abstract | ||
|---|---|---|
Probabilistic conceptual network is a knowledge representation scheme designed for reasoning about concepts and categorical abstractions in utility-based categorization. The scheme combines the formalisms of abstraction and inheritance hierarchies from artificial intelligence, and probabilistic networks from decision analysis. It provides a common framework for representing conceptual knowledge, hierarchical knowledge, and uncertainty. It facilitates dynamic construction of categorization decision models at varying levels of abstraction. The scheme is applied to an automated machining problem for reasoning about the state of the machine at varying levels of abstraction in support of actions for maintaining competitiveness of the plant. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/B978-1-4832-1451-1.50025-1 | UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence |
Keywords | DocType | Volume |
conceptual knowledge,probabilistic network,knowledge representation scheme,hierarchical knowledge,probabilistic conceptual network,belief representation scheme,utility-based categorization,decision analysis,categorical abstraction,categorization decision model,varying level | Journal | abs/1303.1474 |
ISBN | Citations | PageRank |
1-55860-306-9 | 1 | 0.40 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kim Leng Poh | 1 | 62 | 7.36 |
| Michael R. Fehling | 2 | 32 | 15.18 |