Gottfried Schenner

Modeling and solving technical product configuration problems

Abstract This paper describes and evaluates approaches to model and solve technical product configuration problems using different artificial intelligence methodologies. By means of a typical example, the benefits and limitations of different artificial intelligence methods are discussed and a flexible software architecture for integrating different solvers in a product configurator is proposed.

Re)configuration based on model generation

Reconfiguration is an important activity for companies selling configurable products or services which have a long life time. However, identification of a set of required changes in a legacy configuration is a hard problem, since even small changes in the requirements might imply significant modifications. In this paper we show a solution based on answer set programming, which is a logic-based knowledge representation formalism well suited for a compact description of (re)configuration problems. Its applicability is demonstrated on simple abstractions of several real-world scenarios. The evaluation of our solution on a set of benchmark instances derived from commercial (re)configuration problems shows its practical applicability.

Computing product configurations via UML and integer linear programming

The Unified Modelling Language (UML) can be used to specify complex systems: component types are modelled as classes, interdependencies as associations with multiplicities and labels. This paper describes how to handle constraints on associations and multiplicities declaratively by translating them to inequalities over integers without adding complexity. This method provides well-defined semantics and allows for efficient algorithms for reasoning tasks like detecting inconsistencies. We identify some challenges arising from the use of class diagrams for product configuration, and propose solutions for some of them. The paper concludes with the discussion of an example derived from a real-world configuration problem in the railway domain.

Debugging Non-ground ASP Programs with Choice Rules, Cardinality and Weight Constraints

When deploying Answer Set Programming ASP in an industrial context, for instance for re-configurationi¾ź[5], knowledge engineers need debugging support on non-ground programs. Current approaches to ASP debugging, however, do not cover extended modeling features of ASP, such as choice rules, conditional literals, cardinality and weight constraintsi¾ź[13]. To this end, we encode non-ground ASP programs using extended modeling features into normal logic progams; this encoding extends existing encodings for the case of ground programs [4,10,11] to the non-ground case. We subsequently deploy this translation on top of an existing ASP debugging approach for non-ground normal logic programsi¾ź[14]. We have implemented and tested the approach and provide evaluation results.

Twenty-Five Years of Successful Application of Constraint Technologies at Siemens

The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. Because one of the core competencies of Siemens is to provide such highly engineered and customized systems, ranging from solutions for medium-sized and small businesses up to huge industrial plants, the efficient implementation and maintenance of configurators are important goals for the success of many departments. For more than 25 years the application of constraint-based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period. In particular, we highlight the main technology factors regarding knowledge representation, reasoning, and integration which were important for our achievement. Finally we describe selected key application areas where the business success vitally depends on the high productivity of configuration processes.

Combining Heuristics for Configuration Problems Using Answer Set Programming

This paper describes an abstract problem derived from a combination of Siemens product configuration problems encountered in practice. Often isolated parts of configuration problems can be solved by mapping them to well-studied problems for which efficient heuristics exist (graph coloring, bin-packing, etc.). Unfortunately, these heuristics may fail to work when applied to a problem that combines two or more subproblems. In the paper we show how to formulate a combined configuration problem in Answer Set Programming (ASP) and to solve it using heuristics a la hclasp. In addition, we present a novel method for heuristic generation based on a combination of greedy search with ASP that allows to improve the performance of an ASP solver.

OOASP: Connecting Object-Oriented and Logic Programming

Most of contemporary software systems are implemented using an object-oriented approach. Modeling phases – during which software engineers analyze requirements to the future system using some modeling language – are an important part of the development process, since modeling errors are often hard to recognize and correct.

Reconciling Practice and Rigour in Ontology-Based Heterogeneous Information Systems Construction

Ontology integration addresses the problem of reconciling into one single semantic framework different knowledge chunks defined according to its own ontology. This field has been subject of analysis and many consolidated theoretical results are available. Still, in practice, ontology integration is difficult in heterogeneous information systems (HIS) that need to integrate assets already built and running which cannot be changed. Furthermore, in practice, the composed assets are usually not really defined according to an ontology but to a data model which is less rigorous but fit for the purpose of defining a data schema. In this paper, we propose a method for integrating assets participating in a HIS using a domain ontology, aimed at finding an optimal balance between semantic rigour and feasibility in terms of adoption in a real-world setting. The method proposes the use of data models describing the semantics of existing assets; their analysis in order to find commonalities and misalignments; the definition of the domain ontology, considering also other sources as standards, to express the main concepts in the HIS domain; the connection of the local models with this domain ontology; and its abstraction into a metamodel to facilitate further extensions. The method is an outcome of a collaborative software development project, OpenReq, aimed at delivering an ontology for requirements engineering (RE) designed to serve as baseline for the data model of an open platform offering methods and techniques to the RE community. The construction process of this ontology will be used to illustrate the method.

Chapter 22 – S’UPREME

S’UPREME is an in-house configuration system of Siemens AG for the configuration of complex large-scale technical systems, for instance railway safety systems or industrial production systems (see also Chapter 16). This chapter gives a brief overview of the technological background and modeling expressiveness of S’UPREME.