.png){kind=icon}
.png){kind=icon}
Return
Probabilistic partial knowledge handling
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2007.05.014.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper is a short review and comparison of two probabilistic models for uncertain knowledge representation: Bayesian networks and compositional models. We have chosen these two approaches because they represent the same class of distributions and because they are typical representatives of the approaches using conditional (for Bayesian networks) and unconditional (for compositional models) distributions as basic building blocks for model construction. The comparison will be made from the viewpoint of partial knowledge processing, in particular. Here we have in mind not only their capability to create global models from systems of pieces of local knowledge but most of all their efficiency to infer new pieces of local knowledge, different from those forming a generating (input) system. (C) 2007 Elsevier Inc. All rights reserved.
Keywords:
knowledge representation
Bayesian network
operator of composition
compositional model
consistence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.9K
Citations:
5.1K