A METHOD FOR INTEGRATING END-USER PREFERENCES FOR DESIGN EVALUATION IN RULE-BASED SYSTEMS

Expert systems for design often include provisions for comparison of p reliminary design alternatives. Historically, this task has been done on an ad hoc basis (or not at all) due to two difficulties. The first difficulty is design evaluation of multiple attributes. The second is that of taking into account highly subjective end-user preferences. De sign experts have developed techniques which have enabled them to deal with these two difficulties; weighted average methods for the former and heuristic ''rules of thumb'' which categorize end-users for the la tter. Limitations of these techniques are that the accuracy and precis ion of weighted average methods is inadequate, and that the ''rules of thumb'' might be reasonable and valid for most end-users, but not for some others. This paper brings quantitative rigor to the modelling of end-user preferences which is equal to that used in other phases of e ngineering analysis. We present a technique by which a heuristic rule base derived from technical experts can be analyzed and modified to in tegrate quantitative assessment of end-users' subjective preferences. The operations research tool of multiattribute utility analysis is int egrated with artificial intelligence techniques to facilitate prelimin ary evaluation of design alternatives of multiple attributes specific to individual users. The steps of the methodology are: develop the heu ristic rule base, analyze the rule base to separate subjective from ob jective rules, add a subjective multiattribute utility assessment modu le, add an uncertainty assessment module, make objective rules explici t, and express performance attributes in terms of design decision vari ables. The key step is making the distinction between subjective and o bjective aspects of rules, and replacing the former with utility analy sis. These steps are illustrated through an expert system for material s selection for a sailboat mast. Results indicate improved expert syst em performance for both ''typical'' and ''atypical'' end-users.