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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/da7wzr6onfgsplgmbtf6e5d4ke" style="color: black;">Artificial intelligence for engineering design, analysis and manufacturing</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1017/s0890060498124010">doi:10.1017/s0890060498124010</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cncrw5udsfeixfz6xliiv74kfu">fatcat:cncrw5udsfeixfz6xliiv74kfu</a> </span>
more »... g plant. Configuration design was an early success in applying artificial intelligence~AI! techniques, specifically expert or rule-based systems, to practical problems. Among the most famous example is the R1 system for configuring computers and the accompanying XSEL, which were used at Digital Equipment Corporation~McDermott, 1980 , 1981 In the early to mid-1980s many systems and techniques were developed in the research community to solve a variety of configuration-design problems. After this early activity, a plateau was reached in the research community. Because configuration is fundamentally a design activity, research has always been closely aligned with practical applications. It is not surprising, then, that renewed interest in configuration-design research is driven in part by industrial need. In addition, newer techniques have been developed over the past 15 years that have advantages, such as improved knowledge representation, computational performance, and adaptation to new technologies~e.g., the World Wide Web!. In many industries today, there does not exist a "standard" product offering, but products are created specifically for a customer's unique needs, known variously as pick-to-order, assemble-to-order, configure-to-order, and engineer-to-order. Examples include computer and telecommunication equipment, financial services, and even footwear. This type of customization for even the simplest products requires a configuration system to guarantee product accuracy and completeness. In addition, configuration has a direct affect on pricing, the length of the sales cycle, and inventorỹ DeSisto, 1997!. Thus, configuration is a core element of "Technology-Enabled Selling." According to the Gartner Group, this market has experienced 35% annual growth since 1995, and will reach $3.9 billion in software licenses by 2000 Goltermann, 1997!. Companies that do not address these elements will be at a severe competitive disadvantage. To review the history of configuration design from a research perspective, we have asked Dr. David Brown of WPI to submit a position paper. In this paper, entitled Defining Configuring, Prof. Brown revisits the classic definition of configuration design by Mittal and Frayman~1989!, and concludes that there is much left unsaid. To look ahead to future research directions that may impact commercial applications, we have asked Dr. David Franke, chief architect at Trilogy Development Group, to draw on his experiences to identify future avenues of research that will have a direct impact on business. THE GENERAL THEMES OF THIS ISSUE Our aim in this special issue is to motivate renewed interest in configuration-design research and to show a sample of the state-of-the-art. Based on the submissions for this special issue, we are encouraged by the variety of techniques currently being applied to configuration problems. In the paper Towards a General Ontology of Configuration, Timo Soininen, Juha Tiihonen, Tomi Männistö, and Reijo Sulonen attempt to unify configuration terminology as a step toward a general ontology of configuration, which is needed to reuse and share configuration knowledge. This is an important area of research for solving large-scale configuration problems using heterogeneous knowledge sources. Two papers are included from the constraint-based framework. In the first paper, A Classification and Constraint Based Framework for Configuration, Daniel Mailharro presents a framework that models configuration problems using clasification and constraint-satisfaction problem~CSP! techniques. Classification techniques are used to structure domain knowledge to take advantage of inheritance to increase maintainability. CSP concepts are used to represent constraints, identify propagation and solution algorithms, and reason about partial knowledge.
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