In configuration design parts are selected and connected to meet customer specifications and engineering and physical constraints. Specifications include preferences (e.g., “prefer lower cost to higher performance, all things being equal”), bounds on various resources (e.g., “the computer should have four PCI slots”), and other information to customize a configuration. Constraints typically arise from exogenous concerns, such as the available parts, the way parts can interact, and the manufacturing plant.

In this paper we intend to motivate various research areas in configuration, based on our experience in developing commercial configuration solutions. Informal definitions are given for the configuration task and for configuration specification and description languages. We also offer an abstract characterization of the issue addressed in configuration models, most often represented as constraints or rules. With these definitions and examples of configuration problems from various domains, we motivate research in (1) a common ontology for configuration, (2) function representation and functional reasoning in configuration, and (3) scaling configuration to large problems.

This paper is intended to serve as part of the context in which the other papers in this special issue should be read. Its main goal is to revisit the basic definition of the configuration task, on which many people depend, to show some of its flaws, and to point out how it shapes thinking about the problem. We are concerned about characterizing the reasoning processes used to produce a configuration.

This paper describes the technical principles and representation behind the constraint-based, automated configurator COCOS. Traditionally, representation methods for technical configuration have focused either on reasoning about structure of systems or quantity of components, which is not satisfactory in many target areas that need both. Starting from general requirements on configuration systems, we have developed an extension of the standard CSP model. The constraint-based approach allows a simple system architecture, and a declarative description of the different types of configuration knowledge. Knowledge bases are described in terms of a component-centered knowledge base written in an object-oriented representation language with semantics directly based on the underlying constraint model. The approach combines a simple, declarative representation with the ability to configure large-scale systems and is in use for actual production applications.

Configuration assistants are tools for guiding the final user in simple configuration tasks, such as product assembling and customization or study plans generation. For their wide availability, web-based configuration assistants are valuable in fields such as electronic commerce and information services. We describe a general approach for building web-based configuration assistants: from a high-level description of the configuration constraints and of the basic items, given in a declarative language, the hypertext files for user guidance and the Java code for constraint checking are generated. We claim that the general approach of process-oriented configuration, where the user is guided through the configuration process by an explanatory hypertext, as opposed to product-oriented configuration, where one starts from a high-level description of the product of the configuration, is better suited for many application domains.

Representing objects and their interactions can be quite challenging when an application requires many complicated, interconnected objects that are restricted in how they can be instantiated. In this paper, we present our approach to conceptual modelling. We have used this approach with success in a number of applications, the largest of which is the PROSE family of configurators. PROSE was first deployed in 1990 and has been used to configure over 4 billion dollars worth of AT&T and Lucent telecommunications equipment. We will discuss our approach to conceptual modelling, which is based on knowledge representation, show how it meets our representation and reasoning needs, and then discuss the relative merits of the approach.2

Configuration is a complex task generally involving varying measures of constraint satisfaction, optimization, and the management of soft constraints. Although many successful systems have been developed, these are often difficult to maintain and to generalize in rapidly changing domains. In this paper, we consider building intelligent knowledge-based systems with maintainability well to the fore in our requirements for such systems. We introduce two case studies: the initial proof of concept, which was in the domain of computer configuration, and a further field-tested study, the configuration of compressors. Central to our approach is the use of the proof planning technique, and the clean separation of different kinds of knowledge: factual, heuristic, and strategic.

This article presents a generalized ontology of product configuration as a step towards a general ontology of configuration, which is needed to reuse and share configuration knowledge. The ontology presented consists of a set of concepts for representing the knowledge on a configuration and the restrictions on possible configurations. The ontology is based on a synthesis of the main approaches to configuration. Earlier approaches are extended with new concepts arising from our practical experience on configurable products. The concepts include components, attributes, resources, ports, contexts, functions, constraints, and relations between these. The main contributions of this work are in the detailed conceptualization of knowledge on product structures and in extending the resource concept with contexts for limiting the availability and use of resources. In addition, constraint sets representing different views on the product are introduced. The ontology is compared with the previous work on configuration. It covers all the principal approaches, that is, connection-based, structure-based, resource-based, and function-based approaches to configuration. The dependencies between the concepts arising from different conceptualizations are briefly analyzed. Several ways in which the ontology could be extended are pointed out.

A fixed set of components with a fixed set of properties is often regarded as a defining characteristic of configuration design problems. In configuration design for larger and more complex systems (e.g., street cars), however, this often is not really true. The reason is that sometimes the component library offers no component that meets the actual requirements, so that a new component has to be added to the library. In general, configuration design for larger products must be interactive: interactive in the sense that the user controls the configuration process and in the sense that the phases of knowledge acquisition and configuration are partly mixed. This paper describes a method and a corresponding software tool (SyDeR, System Design for Reusability) that support the interactive configuration design of complex products, especially in the tendering phase. It combines three different technologies:

[bull ] structural modeling of technical systems;

[bull ] a library for technical solutions, which is based upon taxonomies and allows the reuse of the technical solutions;

[bull ] constraint techniques to propagate design decisions and check designs for consistency to support interactive configuration design.

This paper gives an overview of the functionality of the SyDeR tool and describes the main ideas behind the modeling language, which is especially oriented towards the requirements of system design problems. It also explains how we integrated structural modeling, taxonomies, and advanced constraint reasoning techniques into real-world applications.

One of the main difficulties with configuration problem solving lies in the representation of the domain knowledge because many different aspects, such as taxonomy, topology, constraints, resource balancing, component generation, etc., have to be captured in a single model. This model must be expressive, declarative, and structured enough to be easy to maintain and to be easily used by many different kind of reasoning algorithms. This paper presents a new framework where a configuration problem is considered both as a classification problem and as a constraint satisfaction problem (CSP). Our approach deeply blends concepts from the CSP and object-oriented paradigms to adopt the strengths of both. We expose how we have integrated taxonomic reasoning in the constraint programming schema. We also introduce new constrained variables with nonfinite domains to deal with the fact that the set of components is previously unknown and is constructed during the search for solution. Our work strongly focuses on the representation and the structuring of the domain knowledge, because the most common drawback of previous works is the difficulty to maintain the knowledge base that is due to a lack of structure and expressiveness of the knowledge representation model. The main contribution of our work is to provide an object-oriented model completely integrated in the CSP schema, with inheritance and classification mechanisms, and with specific arc consistency algorithms.