【摘要】 讨论了基于事例推理(CBR)在人工智能系统中的作用,分析了常用的CBR检索算法,并着重研究了提高系统检索效率问题.针对CBR系统的关键性问题事例检索,提出了一种分级检索算法与最近相邻算法相结合的检索策略(L&NCBR),分析了在精确匹配与非精确匹配情况下该策略的效率,并给出了基于L&NCBR的智能推理系统运行情况分析.结果表明L&NCBR策略在提高系统检索的稳定性和效率方面是有效的.更多还原

【Abstract】 Discusses the role of case-based reasoning(CBR) in artificial intelligence systems and some commonly used CBR algorithms.Then,the efficiency of CBR systems is emphatically studied.A new retrieval algorithm L & NCBR combining the nearest-neighbor algorithm with hierarchical retrieval algorithm is designed aiming at the crucial problem of case-based reasoning system.In addition,the efficiency of the algorithm under both accurate and inaccurate conditions is discussed as well as the running result of the L & NCBR-based intelligent system.The result showed that the strategy can improve the stability of the system and efficiency of retrieval.更多还原