The present invention relates to an inference processing method for solving a problem based on previous cases. More particularly, the present invention relates to an inference processing method whereby not only cases indicating how to solve problems, but also other cases indicating how the cases were used, are used, and if a user modifies a solution, this modification case is learnt to automatically modify a similar solution case which might occur in the future.
Recently, an inference system (expert system) for dealing with expert works has been widely used in practice. Expert knowledge is inputted in an expert system generally in the form of rules.
It is however not easy to transform human intuitive knowledge into rules. It is also difficult to describe a number of rules without any contradiction among them. Accordingly, inputting expert knowledge becomes the most serious obstacle when developing an expert system.
Another inference method (case-based inference) has been studied which uses problem solving cases of experts, i.e., past problems and their expert solutions or ways to solve them. It was thought at the initial stage that if an expert system is developed using case-based inference, most of expert knowledge can be inputted in the form of cases, alleviating the difficulty of inputted rules.
However, there exist only a small number of cases which are quite the same as that of a problem now intended to be solved by using case-based inference. Therefore, it becomes necessary to modify the past solution of a case problem or the past way to solve a case problem so as to apply it to a present problem. For modifying a case, it is necessary to provide an inference system with knowledge used by experts to solve problems based on cases. However, with conventional case-based inference, case modification knowledge has been entered in the form of rules, as discussed in the papers on "Case-Based Reasoning Workshop (1988)", pp. 21 to 30.
In general computer systems including such inference systems, programs and knowledge in each system are required to be modified if the system function becomes insufficient for a user need. Such system modification requires much labor and may be likely to contain new errors.
A learning function has been considered as described in the "Operation Manual, Function" of a Hitachi Japanese word processor WordPal 620 HD, (1988), pp. 14 to 18. According to this function, the system selects one of a plurality of solutions (e.g., kanji candidates in kana-kanji transformation). If a user selects another solution, the system learns the selection the user selected, and selects it in the succeeding operation.