Luca Anselma (Università di Torino, Italy) and Diego Magro (Università di Torino, Italy)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-59904-849-9.ch061
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Configuring means selecting and bringing together a set of given components to produce an aggregate (or a set of aggregates) satisfying some requirements. All the component types are predefined and no new component type may be created during the configuration process. The result of the configuration can be physical objects (such as cars or elevators), non-physical entities (such as compound services or processes) or heterogeneous wholes made of both physical and non-physical parts (such as computer systems with their hardware and software components). The configuration process has to take into consideration both endogenous and exogenous constraints: the former pertain to the type of the assembled object(s) (therefore they hold for all the individuals of that type) and mainly come from the interactions among components, whereas the latter usually represent requirements that the final aggregate(s) should satisfy. All these constraints can be very complex and make the manual solution of configuration problems a very hard task in many cases. The complexity of configuration and its relevance in several application domains have stimulated the interest in its automation. Since the beginning, Artificial Intelligence has provided various effective techniques to achieve this goal. One of the first configurators was also one of the first commercially successful expert systems: a production rule-based system called R1 (McDermott, 1982, 1993). R1 was developed in the early Eighties to configure VAX computer systems, and it has been used for several years by Digital Equipment Corporation. Since then, configuration has gained importance both in industry and in marketing, also due to both the support that it offers to the mass customization business strategy and the new commercial opportunities provided by the Web. Configuration is currently an important application field for many Artificial Intelligence techniques and it is still posing many interesting problems to scientific research.
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The increasing complexity and size of configurable products made it clear that production-rule-based configurators such as R1 are not effective, particularly in the phase of maintenance of knowledge bases. In fact, changing a rule may require, as a side effect, changing several other rules and so on, and, actually, for some products, the component library may change frequently.

To partly address this problem, in current configurator systems, domain knowledge and control knowledge for problem solving are separate. The domain knowledge is represented in a declarative language, and the control knowledge (i.e., inferential mechanisms) is general (i.e., not depending on the particular problem to be solved). This is a common approach in modern knowledge-based systems. A configurator is based on an explicit representation of the general model of the configurable entities, which implicitly represents all the valid product individuals. The reasoning mechanisms implement the control knowledge and they use the domain knowledge to draw inferences and to compute configurations.

Regarding domain knowledge, there is a general agreement about what the concepts to represent are. In (Soininen, Tiihonen, Männistö & Sulonen, 1998) the authors introduce a widely accepted conceptualization for configuration problems. This conceptualization includes the concepts of

  • components, which are the constituents of configurations;

  • parts to describe the compositional structure;

  • ports to model connections and compatibilities between components;

  • resources that are produced, used or consumed by components;

  • functions to represent functionalities;

  • attributes used to describe components, ports, resources and functions;

  • taxonomies in which component, port, resource and function types may be organized in;

  • constraints to specify conditions that configurations must satisfy.

Key Terms in this Chapter

Stable Herbrand Model: A minimal set of facts satisfying a logic program (theory). Each fact in the model is a variable-free atom whose arguments are terms exclusively built through function and constant symbols occurring in the program and whose predicate symbols occur in the program as well. Facts not appearing in the model are regarded as false.

Constraint Satisfaction Problem (CSP): A CSP is defined by a finite set of variables, where each variable is associated with a domain, and a set of constraints over a subset of variables, restricting the possible combinations of values that the variables in the subset may assume. A solution of a CSP is an assignment of a value to each variable that is consistent with the constraints.

Propositional Logic Formula Satisfiability: The task of checking whether it is possible to assign a truth value to every variable that occurs in a propositional formula, such that the truth value of the whole formula equals true.

Mass Customization: A business strategy that combines the mass production paradigm with product personalization. It is closely related to the modularity in product design. This design strategy makes it possible to adopt the mass production model for standard modules, facilitates the management of product families and variants and it leaves room for (various kinds and degrees of) personalization.

Production-Rule-Based System: A system where knowledge is represented by means of production rules. A production rule is a statement composed of conditions and actions. If data in working memory satisfy the conditions, the related actions can be executed, resulting in an update of the working memory.

Description Logics (DL): Logics that are designed to describe concepts and individuals in knowledge bases. They were initially developed to provide a precise semantics for the frame systems and the semantic networks. The typical inference for concepts is checking if a concept is more general than (i.e., subsumes) another one. The typical inference for individuals is checking if an individual is an instance of a concept. Many DL are fragments of first-order logic, while some of them go beyond first order.

Logic Program: A logic theory (possibly containing some extra-logic operators) that can be given a procedural meaning such that the process of checking if a formula is derivable in the theory can be viewed as a program execution.

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