The goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.

In cases where a domain theory can be successfully expressed in a logical formalism and can be used to formulate a task in that domain in mathematical terms, the task of building sound knowledge-based systems is greatly facilitated. However, it is not immediately obvious how the design aspects of such tasks, where these are an important feature of problem solving, can be incorporated in this approach. Design issues differ from search problems in that there may be several choices, each valid in some sense, but not (necessarily) equally good or equally appropriate in the current context. A case study is described in which a methodology is used based on the development of proof plans. The ability to conduct research according to the Popperian framework of hypothesis, validation, testing, and modification in response to empirical evidence – the hypothetico-deductive approach – seems essential to any rigorous scientific endeavor. It is believed that proof planning is a method which readily exploits this inherently incrementalist approach and could prove to be a powerful tool in designing AI systems.

Development of expert systems involves knowledge acquisition that can be supported by applying machine learning techniques. The basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM) is presented. How decision-tree induction is used to build and refine the knowledge base of the process is also discussed.

The idea of developing an intelligent supervisory system with a learning component [Intelligent MAnufacturing FOreman (IMAFO)] that is already implemented is briefly introduced. The results of applying IMAFO for analyzing data from the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information, and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledge base of an expert system) are generated from the rules induced by IMAFO. The procedure to refine these types of rules is also explained.

This paper describes an application of artificial intelligence to support software reuse. We begin by discussing the characteristics of software engineering that establish dynamic reorganization as a requirement for a repository of software artifacts. We then present an experimental system that uses incremental concept formation as the basis for dynamic reorganization, and the conceptual hierarchy that was generated by the system for a set of 67 artifacts. The hierarchy is compared to a hierarchy produced manually by independent investigators, and the automatic hierarchy is evaluated in terms of retrieval efficiency and retrieval reliability. The paper ends with a discussion of three projects that share similar objectives with our work.

Contributions of general design theory (GDT) proposed by Yoshikawa for the development of advanced CAD (computer-aided design) and for innovative design from the research results of a group at the University of Tokyo are illustrated. First, the GDT that formalizes design knowledge based on axiomatic set theory is reviewed. Second, this theoretical result is tested against experimental work on design processes. Although in principle the theoretical results agree with the experimental findings, some problems can be pointed out. From these problems a new design process model, called the refinement model, is established, which has better agreement with the experimental findings. This model implies three guiding principles in developing a future CAD system. One is that future CAD requires a mechanism for physics-centered modeling and multiple model management. Second, a mechanism for function modeling is also required, and the FBS (function-behavior-state) modeling is proposed. Third, intention modeling is also proposed for recording decision-making processes in design. These advanced modeling techniques enable creative, innovative designs. As an example, the design of self-maintenance machines is illustrated. This design example utilizes design knowledge intensively on a knowledge-intensive CAD. This is a new way of engineering and can be called knowledge-intensive engineering. The design of self-maintenance machines is, therefore, an example of knowledge-intensive design of knowledge-intensive products, which demonstrates the power of the design methodology derived from the GDT.

One of the products of engineering, besides constructed artifacts, is design documentation. To understand how design participants use documentation, designers and typical documentation users were interviewed and protocols were taken of them both creating and using design documentation. The protocols were taken from realistic projects of preliminary design for heating, ventilation, and air conditioning systems (HVAC). The studies of document creation and use revealed three important issues: (1) design participants not only look up design facts; they frequently access documents to obtain information about the rationale for design decisions; (2) the design rationale that they see often is missing from the documents; and (3) design requirements change frequently over a project life cycle so that design documents are often inconsistent and out of date. Recognizing these documentation issues in design practice, a new approach was developed in which documents are no longer static records, but rather interactive design models supporting a case. The feasibility of the approach was demonstrated by constructing a running system and testing it with designers on realistic problems. The costs and benefits of creating and using documentation of design rationale also were analyzed. In particular, the active documents approach was evaluated for a routine, preliminary design in domains where community practice is widely shared and largely standardized. The approach depends on the feasibility of creating a parametric design model for the design domain.

This annotated bibliography includes a small sample of sources on various aspects of research methodology from diverse disciplines that influence research on artificial intelligence techniques in engineering design analysis and manufacturing (AIEDAM). Some of these sources are extended edited volumes containing many relevant contributions and pointing to additional references. These volumes are marked by a preceding bullet (•). The bibliography is not comprehensive; it covers only several important subjects, and in each subject it lists several representative contributions ordered chronologically.