A precise market model for a well-defined class of distributed configuration design problems is presented. Given a design problem, the model defines a computational economy to allocate basic resources to agents participating in the design. The result of running these “design economies” constitutes the market solution to the original problem. After defining the configuration design framework, the mapping to computational economies and the results to date are described. For some simple examples, the system can produce good designs relatively quickly. However, analysis shows that the design economies are not guaranteed to find optimal designs, and some of the major pitfalls are identified and discussed. Despite known shortcomings and limited explorations thus far, the market model offers a useful conceptual viewpoint for analyzing distributed design problems.

SALT is the highly influential system implemented by Marcus (1988), which is able to acquire knowledge and solve problems using the propose-and-revise strategy. The primary task to which this has been applied is the design of elevators, or lifts (Marcus et al., 1988). Recently we have re-implemented and extended the system at Aberdeen, S-SALT, the new system, has the following enhancements:

The use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.

As designs exist to satisfy some purpose or function, knowledge of functionality is essential in a wide variety of designrelated activities, including generation and modification of designs, comparison, evaluation and selection of designs, and explanation, diagnosis or repair of designs. Functional modelling refers to a wide variety of approaches to model a design and its requirements from its functional aspects so as to allow reasoning about its functionality for various activities such as the above.

Serving as a guest editor of this special journal issue, I review in this introductory paper some of the important issues concerning applying case-based reasoning (CBR) techniques to the design domain. Through discussions of these issues, I attempt to give the current status of the case-based design field, with surveys of existing case-based design systems and a summary from the first workshop held on this subject. For those who are less familiar with CBR, this paper also contains a section on what CBR method is and how it is contrasted with other reasoning approaches. The last section of this article introduces the papers included in this issue.

MAPCon II is the second generation (Muralidhar and Irish, IEEE Journal on Selected Areas in Commumcctions 6(5), 869–873, 1988) of an expert system that interactively guides a user in performing off-line configuration for local area networks that use MAP, the manufacturing automation protocol. This paper describes the configuration task in general and MAPCon in particular.

Though MAPCon's purpose is off-line configuration, its problem domain requires that it accomplish other reasoning objectives in addition to those commonly associated with configuration. It is in the process of being expanded into an on-line network supervisor. We develop a taxonomy of reasoning objectives and show how MAPCon combines two different kinds of reasoning to accomplish its objectives. Our experience confirms that of other researchers, and suggests that building robust, practical systems will require us to understand more clearly the interfaces among different reasoning objectives.

The paper has four parts: 1. a definition of configuration and other reasoning objectives; 2. a summary of the problem domain in which MAPCon operates; 3. a description of MAPCon as the user sees it; 4. a look ‘under the hood’ to see how MAPCon combines different objectives.

The paper describes the kinematic analysis of a new translational parallel manipulator (TPM). The manipulator consists of a fixed base, with a moving platform connected to the base by three identical legs with PUPR chains. The axes of the actuated motions are orthogonal. This configuration provides a very simplified kinematic analysis with fully decoupled input–output linear equations, absence of translational singularities, isotropy and ease of determining the workspace for the moving platform.

Currently design documentation rarely records the designer's decision process or the reasons behind those decisions. This paper describes an effort to improve design documentation by having the computer act as an intelligent apprentice to the designer to capture the rationale during the design process. The apprentice learns about the features that make a specific case different from the standard. Whenever the designer proposes a design action that differs from the apprentice's expectations, the interface will ask for the designer for justifications to explain the differences. Later queries for design rationale are answered using a combination of the apprentice's domain knowledge and the designer-supplied justifications. The apprentice model is being implemented in a prototype system called ADD (Augmenting Design Documentation). The initial focus of the work is on HVAC (Heating, Ventilation, and Air Conditioning) design. Our starting point for implementing the apprentice model is observing how people develop HVAC system designs and then explain those designs.

This article presents our approach for conflict management in our knowledge acquisition tool KATEMES, aimed at tackling multiple experts. We offer a method for helping the knowledge engineer to detect expertise conflicts in the framework of the KADS knowledge acquisition method. We also propose techniques for conflict management through comparison of knowledge graphs.

An efficient locally minimum-time trajectory planning algorithm for coordinately operating multiple robots is introduced. The task of the robots is to carry a common rigid object from an initial position to a final position along a given path in three-dimensional workspace in minimum time. The number of robots in the system is arbitrary. In the proposed algorithm, the desired motion of the common object carried by the robots is used as the key to planning of the trajectories of all the non-redundant robots involved. The search method is used in the trajectory planning. The planned robot trajectories satisfy the joint velocity, acceleration and torque constraints as well as the path constraints. The other constraints such as collision-free constraints, can be easily incorporated into the trajectory planning in future research.

The process of reviewing test data for anomalies after a firing of the Space Shuttle Main Engine (SSME) is a complex, time-consuming task. A project is under way to provide the team of SSME experts with a knowledge-based system to assist in the review and diagnosis task. A model-based approach was chosen because it can be adapted to changes in engine design, is easier to maintain, and can be explained more easily. A complex thermodynamic fluid system like the SSME introduces problems during modeling, analysis, and diagnosis which have as yet been insufficiently studied. We developed a qualitative constraint-based diagnostic system inspired by existing qualitative modeling and constraint-based reasoning methods which addresses these difficulties explicitly. Our approach combines various diagnostic paradigms seamlessly, such as the model-based and heuristic association-based paradigms, in order to better approximate the reasoning process of the domain experts. The end-user interface allows expert users to actively participate in the reasoning process, both by adding their own expertise and by guiding the diagnostic search performed by the system.

This paper focusses on how conflicts that arise during a design process and the management of conflicts can be modelled. A number of possible conflict types are distinguished and it is described how each of them can be detected during the design process, using an explicit meta-representation. Furthermore, it is shown how these conflict types can be analyzed and managed by means of strategic meta-knowledge about design processes.

Analogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.