The refitting of plans by a human expert

During the course of the development of a Case-Oriented Expert System for situated applications additional cases were needed. The required cases were obtained by having a human expert refit old solutions to new problems and the structural relations between source and target cases were analyzed: A higher degree of reuse of the old cases was found when the expert could apply derivational reasoning and a uniform design rationale (i.e. the solution of the source was generated by the expert himself) than when the expert could only analyze structural relationships (i.e. the source solution was constructed by some one else). Except with very obvious cases, it was also found, that different experts perceive different cases as the most similar source to a given target problem. The results also indicate for user-situated applications of expert systems

The model-based construction of a case-oriented expert system

Second generation expert systems should be based upon an expert\u27s high level understanding of the application domain and upon specific real world experiences. By having an expert categorize different types of relevant experiences and their components, hierarchies of abstract problems and operator classes are determined on the basis of the expert\u27s accumulated problem solving experiences. The expert\u27s global understanding of the domain is integrated with the experiences by a model of expertise. This model postulates problem classes at different levels of abstractions and associated skeletal plans. During a consultation with the expert system previously unseen types of input may be used to delineate a new problem. The application of the expert system can thus be situated in changing environments and contexts. With increasing dissimilarity between the cases that were analyzed during knowledge acquisition and the specific problem that is processed at the time of the application of the system, its performance gracefully degrades by supplying a more and more abstract skeletal plan. More specifically, the search space which is represented by the skeletal plan increases until the competence of the system is exceeded. This paper describes how such a case-oriented expert system is developed for production planning in mechanical engineering

The model-based construction of a case-oriented expert system

Second generation expert systems should be based upon an expert's high level understanding of the application domain and upon specific real world experiences. By having an expert categorize different types of relevant experiences and their components, hierarchies of abstract problems and operator classes are determined on the basis of the expert's accumulated problem solving experiences. The expert's global understanding of the domain is integrated with the experiences by a model of expertise. This model postulates problem classes at different levels of abstractions and associated skeletal plans. During a consultation with the expert system previously unseen types of input may be used to delineate a new problem. The application of the expert system can thus be situated in changing environments and contexts. With increasing dissimilarity between the cases that were analyzed during knowledge acquisition and the specific problem that is processed at the time of the application of the system, its performance gracefully degrades by supplying a more and more abstract skeletal plan. More specifically, the search space which is represented by the skeletal plan increases until the competence of the system is exceeded. This paper describes how such a case-oriented expert system is developed for production planning in mechanical engineering

Knowledge Acquisition by Generating Skeletal Plans from Real World Cases

Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation- based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an instrument for adjusting the applicability of the acquired skeletal plans to that class. The four phases of the proposed procedure are constituted as follows: In the first phase, the execution of the source plan is simulated, and explanations for the effects of the occurred operators are constructed. In the second phase, the generalization of these explanations is performed with respect to a criterion of operationality which specifies the vocabulary for defining abstract operators for the skeletal plan. The third phase, a dependency analysis of the resulting operator effects, unveils the interactions of the concrete plan which are substantial for the specified class. In the forth phase, the concept descriptions for the abstract operators of the skeletal plan are formed by collecting and normalizing the important constraints for each operation that were indicated by the dependencies. With this procedure sophisticated planning solutions from human experts can be generalized into skeletal plans and consequently be reused by a planning system in novel situations

Knowledge Acquisition by Generating Skeletal Plans from Real World Cases

Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation- based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an instrument for adjusting the applicability of the acquired skeletal plans to that class. The four phases of the proposed procedure are constituted as follows: In the first phase, the execution of the source plan is simulated, and explanations for the effects of the occurred operators are constructed. In the second phase, the generalization of these explanations is performed with respect to a criterion of operationality which specifies the vocabulary for defining abstract operators for the skeletal plan. The third phase, a dependency analysis of the resulting operator effects, unveils the interactions of the concrete plan which are substantial for the specified class. In the forth phase, the concept descriptions for the abstract operators of the skeletal plan are formed by collecting and normalizing the important constraints for each operation that were indicated by the dependencies. With this procedure sophisticated planning solutions from human experts can be generalized into skeletal plans and consequently be reused by a planning system in novel situations

Knowledge Acquisition by Generating Skeletal Plans from Real World Cases

. Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation-based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an instrument for adjusting the applicability of the acquired skeletal plans to that class. The four phases of the proposed procedure are constituted as follows: In the first phase, the execution of the source plan is simulated, and explanations for the effects of the occurred operators are constructed. In the second phase, the generalization of these explanations is performed with respect to a criterion of operationality which specifies the vocabulary for defining abstract operators for the skeletal plan. The third phase, a dependency analysis of the resulting operator effects, unveils the int..