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Configuration is a branch of Artificial Intelligence that deals with putting together complicated systems from a collection of simpler parts. One of the most successful application areas for expert systems and AI, in general, is knowledge-based configuration. Configuration systems have been created and used in various fields, including electronics, computers, telecommunications production, chemical design, and industrial facility construction. This paper aims to provide an overview of the many methodologies used in the field.

The complexity of configuration and its importance in a variety of application fields have sparked interest in automating it. Since its inception, Artificial Intelligence has developed several successful approaches to achieving this goal. One of the earliest commercially successful expert systems was a rule-based production system named R1, one of the first configurators (McDermott, 1982, 1993). R1 was created in the early 1980s to set up VAX computer systems, and Digital Equipment Corporation has used it for numerous years.

Even though there is agreement on the problem’s conceptualization, there is a wide range of configuration approaches based on different AI paradigms. Current methods to the configuration can be divided into two categories: constraint-based frameworks and logic-based frameworks. Constraint-based frameworks focus on the combinatorial elements of configuration issues with enormous search spaces and few solutions. In contrast, logic-based frameworks focus on describing the product’s compositional structure.