Generative constraint-based configuration of large technical systems

Abstract

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.

A Distributed Generative CSP Framework for Multi-site Product Configuration

“…To represent these problems we employ an extended formalism that complies to the specifics of configuration and other synthesis tasks where problem variables representing components of the final system are generated dynamically as part of the solution process because their total number cannot be determined beforehand. The framework is called generative CSP (GCSP) [6,19]. This kind of dynamicity extends the approach of dynamic CSP (DCSP) formalized by Mittal and Falkenhainer [12], where all possibly involved variables are known from the beginning.…”

“…The dynamicity occuring in a GCSP differentiates from the one described in [4] in the sense that a GCSP is extended in order to find a consistent solution and the latter has already a solution and is extended due to influence from the outside world (e.g., additional constraints) that necessitates finding a new solution. Here we give a definition of a GCSP that abstracts from the configuration task specific formulation in [19] and applies to the wider range of synthesis problems. By generating additional variables, a previously unsolvable CSP can become solvable, which is explained by the existence of variables that hold the number of variables.…”

“…When modeling a configuration problem, variables representing named connection points between components, i.e., ports, will have references to other components as their domain. Consequently, we need variables whose domain varies depending on the size of a set of specific variables [19].…”

“…Consequently, substitution of any generated variable (i.e., x ∈ X \ X 0 ) by a newly generated variable with equal type, index and value assignment has no effect on the consistency of generic constraints. Our GCSP definition extends the definition from [19] in the sense that a finite set of variable types T is given and during problem solving variables having any of these types can be generated, whereas in [19] only variables of a single type, i.e., component variables, can be created. Current CSP implementations of configuration systems (e.g., [10] [5]) use a type system for problem variables, where new variable instances, having one of the predefined types, are dynamically created.…”

“…A product configurator can be therefore seen as an agent with private knowledge that acts on behalf of its company and cooperates with other agents to solve a configuration task. This paper abstracts the centralized definition of a configuration task in [19] to a more general definition of a generative CSP that is also applicable to the wider range of synthesis problems. Furthermore, we propose a framework that allows us to extend DisCSPs to handle distributed configuration tasks by integrating the innovative aspects of local generative CSPs:…”