Introducing LoCo, a Logic for Configuration Problems

In this paper we present the core of LoCo, a logic-based high-level representation language for expressing configuration problems. LoCo shall allow to model these problems in an intuitive and declarative way, the dynamic aspects of configuration notwithstanding. Our logic enforces that configurations contain only finitely many components and reasoning can be reduced to the task of model construction.Comment: In Proceedings LoCoCo 2011, arXiv:1108.609

LoCo - a logic for configuration problems

This thesis deals with the problem of technical product configuration: Connect individual components conforming to a component catalogue in order to meet a given objective while respecting certain constraints. Solving such configuration problems is one of the major success stories of applied AI research: In industrial environments they support the configuration of complex products and, compared to manual processes, help to reduce error rates and increase throughput. Practical applications are nowadays ubiquitous and range from configurable cars to the configuration of telephone communication switching units. In the classical definition of a configuration problem the number of components to be used is fixed while in practice, however, the number of components needed is often not easily stated beforehand. Existing knowledge representation (KR) formalisms expressive enough to deal with this dynamic aspect of configuration require that explicit bounds on all generated components are given as well as extensive knowledge about the underlying solving algorithms. To date there is still a lack of high-level KR tools being able to cope with these demands. In this work we present LoCo, a fragment of classical first order logic that has been carefully tailored for expressing technical product configuration problems. The core feature of LoCo is that the number of components used in configurations does not have to be finitely bounded explicitly, but instead is bounded implicitly through the axioms. We identify configurations with models of the logic; hence, configuration finding becomes model finding. LoCo serves as a high-level representation language which allows the modelling of general configuration problems in an intuitive and declarative way without the need of having knowledge about underlying solving algorithms; in fact, the specification gets automatically translated into low-level executable code. LoCo allows translations into different target languages. We present the language, related algorithms and complexity results as well as a prototypical implementation via answer-set programming.</p

LoCo — A Logic for Configuration Problems

LoCo is a fragment of classical first order logic tailored for expressing configuration problems. The core feature of LoCo is that the number of components used in configurations does not have to be finitely bounded explicitly, but instead is bounded implicitly through the axioms. Computing configurations reduces to model-finding. We present the language, related algorithms and complexity results as well as a prototypical implementation via answer set programming

LoCo - a logic for configuration problems

This thesis deals with the problem of technical product configuration: Connect individual components conforming to a component catalogue in order to meet a given objective while respecting certain constraints. Solving such configuration problems is one of the major success stories of applied AI research: In industrial environments they support the configuration of complex products and, compared to manual processes, help to reduce error rates and increase throughput. Practical applications are nowadays ubiquitous and range from configurable cars to the configuration of telephone communication switching units. In the classical definition of a configuration problem the number of components to be used is fixed while in practice, however, the number of components needed is often not easily stated beforehand. Existing knowledge representation (KR) formalisms expressive enough to deal with this dynamic aspect of configuration require that explicit bounds on all generated components are given as well as extensive knowledge about the underlying solving algorithms. To date there is still a lack of high-level KR tools being able to cope with these demands. In this work we present LoCo, a fragment of classical first order logic that has been carefully tailored for expressing technical product configuration problems. The core feature of LoCo is that the number of components used in configurations does not have to be finitely bounded explicitly, but instead is bounded implicitly through the axioms. We identify configurations with models of the logic; hence, configuration finding becomes model finding. LoCo serves as a high-level representation language which allows the modelling of general configuration problems in an intuitive and declarative way without the need of having knowledge about underlying solving algorithms; in fact, the specification gets automatically translated into low-level executable code. LoCo allows translations into different target languages. We present the language, related algorithms and complexity results as well as a prototypical implementation via answer-set programming.This thesis is not currently available in ORA

Tackling the Partner Units Configuration Problem ∗

The Partner Units Problem is a specific type of configuration problem with important applications in the area of surveillance and security. In this work we show that a special case of the problem, that is of great interest to our partners in industry, can directly be tackled via a structural problem decompostion method. Combining these theoretical insights with general purpose AI techniques such as constraint satisfaction and SAT solving proves to be particularly effective in practice.

Structural Decomposition Methods and What They are Good For

This paper reviews structural problem decomposition methods, such as tree and path decompositions. It is argued that these notions can be applied in two distinct ways: Either to show that a problem is efficiently solvable when a width parameter is fixed, or to prove that the unrestricted (or some width-parameter free) version of a problem is tractable by using a width-notion as a mathematical tool for directly solving the problem at hand. Examples are given for both cases. As a new showcase for the latter usage, we report some recent results on the Partner Units Problem, a form of configuration problem arising in an industrial context. We use the notion of a path decomposition to identify and solve a tractable class of instances of this problem with practical relevance

Structural Decomposition Methods and What They are Good For

This paper reviews structural problem decomposition methods, such as tree and path decompositions. It is argued that these notions can be applied in two distinct ways: Either to show that a problem is efficiently solvable when a width parameter is fixed, or to prove that the unrestricted (or some width-parameter free) version of a problem is tractable by using a width-notion as a mathematical tool for directly solving the problem at hand. Examples are given for both cases. As a new showcase for the latter usage, we report some recent results on the Partner Units Problem, a form of configuration problem arising in an industrial context. We use the notion of a path decomposition to identify and solve a tractable class of instances of this problem with practical relevance