RIA: Learning Domain Structure and Reasoning with it in Environments of Uncertain Knowledge

这项研究解决了情景模型的自动假设问题，这些模型是推理和决策所需的上下文。本研究涉及三个既不同又相关的方面：1.从领域数据中学习领域属性之间的定性关系及其相关的概率不确定性知识；2.将领域的不确定性知识和其他已知的领域因果定性关系集成到一个单一的图形知识结构中；以及3.从上一步获得的领域知识的中间表示中假设小而有趣的情景模型。对数据库中定性关系的学习被扩展到包括获取属性之间的时间依赖关系，以及当可用数据是多数据库形式时的情况。这种学习方法也被应用于不完备数据库的问题。试图将基于知识的态势模型动态假设扩展应用于路径规划和时间建模问题。还研究了以不同的精度和确定性水平指定情况模型的问题。//

This research addresses the issue of automated hypothesization of situation models that are needed as contexts for reasoning and decision making. The three different but related aspects that are addressed by this research are: 1. Learning from domain data the qualitative relationships among domain attribute and their associated probabilistic uncertainty knowledge; 2. Integration of uncertainty knowledge and other known causal qualitative relationships of the domain into a single graphical knowledge structure; and 3. Hypothesization of small and interesting situation models from the intermediate representation of domain knowledge acquired in the preceding step. The learning of qualitative relationships from databases is extended to include acquisition of temporal dependencies among attributes, and also to the cases when the available data is in the form of multi- databases. This learning method is also sought to be applied to the problem of incomplete databases. The knowledge based dynamic hypothesization of situation models is sought to be extended to apply to path planning and temporal modeling problems. The issue of specifying a situation model at varying levels of precision and certainty are also examined.//