多Agent系统中基于Rough集的推理

定义了多Ａｇｅｎｔ系统中的推理模型，建立了在该模型下的Ｒｏｕｇｈ集和基于Ｒｏｕｇｈ信方法的Ｒｏｕｇｈ包含计算或称集合连接计算，在知识发现和数据挖掘中，集合之间往往不是给出它们的相等性，而是讨论它们之间的Ｒｏｕｇｈ包含或连接。因为在不同的Ａｇｅｎｔ中集合之间关系的精确和一致解释往往是不容易获得的。一般说来，一条基于决策表上的规则，满足前提公式个体的集合包含于满足结论公式个体的集合常常是用一种支持值和

THE INFERENCE BASED ON ROUGH SETS IN MULTI-AGENT SYSTEMS

In this paper, a reasoning model in multi agent systems is defined. The rough sets in the models, and the rough inclusion calculus of sets or set connection computing based on rough set approach are established. The equality between sets can't be given in knowledge discovery and data mining; but only the rough inclusion between them is discussed, because it is often not easy to obtain the exact and consistent interpretation between sets in different agent models. In general, an association rule based on decision table often describes, by the support value and confidence coefficients, the degree by which the set of objects satisfying the formula on the left hand side of the association rule is included in the set of objects satisfying the formula on the right hand side of the association rule. Reasoning in distributed environment often requires that a learning interface between multi agents be offered. The interface is an approximate space AS or inference model IM of multi agent defined in this paper. All operations are performed on data table or decision table. Hence, a composite and included operation is defined on the interface(AS or IM). Thus it is possible to obtain the approximation of definable set performed by cooperation between multi agents and the rough included or connection computing between sets in a distributed environment. It is shown with real examples that it is feasible to achieve some goal by actions of multi agents and cooperation between agents in a distributed environment.