Toward truly intelligent information systems-from expert systems to automatic programming

Abstract: A method of developing an innovative information technology is discussed. It includes a new modeling scheme named multi-strata modeling and enables the user to represent complex systems involving human beings and their intentions in detail. The concept and the basic ideas of this modeling are discussed with some applications. In particular it is used to specify computer programs as a part of a model and to generate the programs automatically. In this way it establishes a new human-computer relation.

Abstract: A way of integrating the different information processing methods is discussed. The types of integrations are defined by the relations between the different information processing methods to be integrated. By classifying these methods the type of integration is classified. This discussion reveals the most feasible way of integrating the different schemes. It is a sequence of integrating the methods via knowledge processing. Based on this discussion two important types of integrations are studied.

Abstract: Ways of building a problem model including an object and persons and of specifying programs in this model is discussed. It enables automation of the following programming process. A high-level intelligent system is needed for the purpose. A new modeling scheme named multi-strata modeling is introduced as a key concept to make computer system intelligent. This approach of specifying and generating program was applied to a car electronic system.

Abstract: This paper discusses an important issue on the intelligent systems to solve large scale problem, especially on the way of making computer programs automatically. Since an activity that human beings design a problem solving system is a kind of problem solving, a new modeling scheme that can deal with multi-level structures is necessary for representing problem solving itself. A large scale system used repeatedly should be a procedural program and not exploratory one. A method of translating modeling results into a procedural program is proposed in this paper.

Abstract: In June of 1983, our expert systems research group at Carnegie Mellon University began to work actively on automating knowledge acquisition for expert systems. In the last five years, we have developed several tools under the pressure and influence of building expert systems for business and industry. These tools include the five described in chapters 2 through 6 - MORE, MOLE, SALT, KNACK and SIZZLE. One experiment, conducted jointly by developers at Digital Equipment Corporation, the Soar research group at Carnegie Mellon, and members of our group, explored automation of knowledge acquisition and code development for XCON (also known as R1), a production-level expert system for configuring DEC computer systems. This work influenced the development of RIME, a programming methodology developed at Digital which is the subject of chapter 7. This book describes the principles that guided our work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise. of the work, brought out in the introductory chapter, is A common theme that much power can be gained by understanding the roles that domain knowledge plays in problem solving. Each tool can exploit such an understanding because it focuses on a well defined problem-solving method used by the expert systems it builds. Each tool chapter describes the basic problem-solving method assumed by the tool and the leverage provided by committing to the method."

Abstract: Although efforts, some successful, to develop expert systems (application systems that can perform knowledge-intensive tasks) have been going on now for almost 20 years, we are not yet very good at describing the variations in problem-solving methods that these systems use, nor do we have much of an understanding of how to characterize the methods in terms of features of the types of tasks for which they are appropriate. This chapter takes a few steps toward creating a taxonomy of methods -- a taxonomy that identifies some of the discriminating characteristics of the methods expert systems use and that suggests how methods can be mapped onto tasks.

Abstract: This paper describes a system named AA1 (Articulation Aid 1) which aids human users in the formation of new concepts in the domain of engineering and science. From the viewpoint of concept formation, one main process of creation is divergent thinking in which broad alternatives are searched, and another process is convergent thinking in which a unique solution is sought. From the viewpoint of human activities, creation also includes the aspect of collaboration among people and the aspect of individual reflection, although they are interrelated. AA1, the system presented in this paper, supports divergent thinking during individual reflection. Engineers and scientists usually scrawl many notes on paper while exploring new possible concepts in the divergent thinking process. A system is needed to reflect the fragments of concepts that are not articulated yet and thereby stimulate the formation of new concepts. AA1 builds a two-dimensional space from the words the user provides. Looking at this space and other precedent spaces, the user can form new concepts little by little. The main feature of AA1 different, from existing hypermedia systems and CSCW systems is the strategy for building the space presented to the user. The system is as nonprescriptive as possible, but it gives stimulation for the user to form concepts that he could not by using only pencil and paper. Experimentation has shown that the space which AA1 displays can effectively help the user to build new concepts. The most prominent effect is that empty regions in the space automatically configured by the system often lead to new concepts. >

Abstract: Workers in artificial intelligence (AI) have developed many interactive programs that assist in the knowledge-acquisition process. Because of the diverse nature of these tools, it is often difficult to understand how each one relates to all the others. This paper describes a taxonomy for knowledge-acquisition aids that is based on the terms and relationships that a given tool uses to establish the semantics of a user's entries. Such semantic assumptions, or conceptual models, have important implications for how a knowledge-acquisition tool is used and to what degree it can assist its users in analysing new applications at the knowledge level. Furthermore, when the conceptual model of a knowledge-acquisition tool can be made explicit, knowledge engineers can use metalevel programs to edit that conceptual model, creating knowledge-acquisition aids that are custom-tailored for particular applications. One such metalevel tool, PROTEGE, has been developed to allow editing of the conceptual models of programs that acquire knowledge for tasks that can be solved via the method of skeletal-plan refinement.