FUNDAMENTAL-CONCEPTS OF QUALITATIVE PROBABILISTIC NETWORKS
FUNDAMENTAL-CONCEPTS OF QUALITATIVE PROBABILISTIC NETWORKS
复制标题
DOI：
10.1016/0004-3702(90)90026-v
复制
发表时间：
1990-08-01
影响因子：
14.4
通讯作者：
WELLMAN, MP
中科院分区：
文献类型：
--
作者：
WELLMAN, MP
概率关系的图形表示最近在人工智能中受到了相当大的关注。定性概率网络从通常的数字表示中抽象出来，只编码定性关系，这是变量联合概率分布的不等式约束。尽管这些约束条件不足以唯一地确定概率，但它们被设计用来证明推导一类暗示有用决策属性的相对可能性结论的合理性。定义了两类定性关系，每一类都是一组变量上单调性约束的概率形式。定性影响描述了两个变量之间关系的方向。定性协同作用描述了影响之间的相互作用。所选择的概率定义证明了基于网络图形操作的合理和有效的推理程序。这些程序回答有关网络中分离变量之间的定性关系的问题，并确定决策变量的最优分配的结构性质。
Graphical representations for probabilistic relationships have recently received considerable attention in AI. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability distribution over the variables. Although these constraints are insufficient to determine probabilities uniquely, they are designed to justify the deduction of a class of relative likelihood conclusions that imply useful decision-making properties.Two types of qualitative relationship are defined, each a probabilistic form of monotonicity constraint over a group of variables. Qualitative influences describe the direction of the relationship between two variables. Qualitative synergies describe interactions among influences.The probabilistic definitions chosen justify sound and efficient inference procedures based on graphical manipulations of the network. These procedures answer queries about qualitative relationships among variables separated in the network and determine structural properties of optimal assignments to decision variables.