基于优先解释的不完全信息推理及其应用

Precedent Interpretation Based Incomplete Information Reasoning and Its Application

不完全信息下的近似推理是知识工程面临的困难问题之一．文章提出了一种具有非单调性质的优先逻辑程序理论．该理论能够对知识的解释进行综合评判，进而优选解释，使其成为现有知识的最佳理论逼近，达到在择优意义下的理论完全化，避免了对知识的完全性及一致性要求．为获取应用领域的优先逻辑程序，基于归纳逻辑程序设计技术设计了一种多方法归纳学习算法，该算法具有较强的归纳能力．此理论与算法已应用在８６３农业专家系统中。

Approximate reasoning with the incomplete information is one of the difficulties that the knowledge engineering has faced. A precedent logic program theory with the property of nonmonotonicity is proposed in this paper. The synthesis evaluation for the interpretation of knowledge can be taken with the theory, such that the optimal selection of interpretation is made possible which becomes the best approach to the current knowledge. The theory completion in the significance of optimal selection is achieved and the requirement of completion and consistency of knowledge are avoided. To acquire the precedent logic programs in the applications, based on an inductive logic programming, learning algorithm is presented which incorporates the multiple inductive methods and has greater ability of induction. The presented theory and the algorithm have been applied in an expert system and gained satisfactory results.