Alois Haselböck

Generative constraint-based configuration of large technical systems

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.

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.

Challenges of knowledge evolution in practice

As knowledge changes over time, its representation in knowledge-based systems along with the existing instances e.g., configured products, schedules, plans, documents, web sites, etc. must be changed, too. This paper enumerates some of the most important challenges which arise in practice when changing a knowledge base: redesign of the knowledge base, schema evolution of the data bases, upgrade of configuration instances, adaptation of solver, UI, I/O and test suites. Partially, there are research theories for some of these challenges, but only few of them are already available in tools and frameworks. We cannot provide solutions here, but we want to stimulate research in knowledge evolution with a representative set of industrial challenges.Product configuration is a prominent case where the use of knowledge-based AI technologies has been well established over the last years. We use a self-contained real-world example from the field of configuration to describe the challenges.

Configuration of Cardinality-Based Feature Models Using Generative Constraint Satisfaction

Existing feature modeling approaches and tools are based on classical constraint satisfaction which consists of a fixed set of variables and a fixed set of constraints on these variables. In many applications however, features may not only be selected but cloned so that the numbers of involved variables and constraints are not known from the beginning. We present a novel configuration approach for corresponding cardinality-based feature models based on the formalism of generative constraint satisfaction which - in extension to many existing approaches - is able to handle constraints in the context of multiple (cloned) features (e.g., by automatically creating new feature clones on the fly).

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.

Generation of conjoint domain models for system-of-systems

Software solutions in complex environments, such as railway control systems or power plants, are assemblies of heterogeneous components, which are very large and complex systems themselves. Interplay of these systems requires a thorough design of a system-of-systems (SoS) encompassing the required interactions between the involved systems. One of the challenges lies in reconciliation of the domain data structures and runtime constraints to ensure consistency of the SoS behavior. In this paper, we present a generative approach that enables reconciliation of a common platform based on reusable domain models of the involved systems. This is comparable to a product line configuration problem where we generate a common platform model for all involved systems. We discuss the specific requirements for model composition in a SoS context and address them in our approach. In particular, our approach addresses the operational and managerial independence of the individual systems and offers appropriate modeling constructs. We report on our experiences of applying the approach in several real world projects and share the lessons learned.

Using Signifiers for Data Integration in Rail Automation.

In Rail Automation, planning future projects requires the integration of business-critical data from heterogeneous data sources. As a consequence, data quality of integrated data is crucial for the optimal utilization of the production capacity. Unfortunately, current integration approaches mostly neglect uncertainties and inconsistencies in the integration process in terms of railway specific data. To tackle these restrictions, we propose a semi-automatic process for data import, where the user resolves ambiguous data classifications. The task of finding the correct data warehouse classification of source values in a proprietary, often semi-structured format is supported by the notion of a signifier, which is a natural extension of composite primary keys. In a case study from the domain of asset management in Rail Automation we evaluate that this approach facilitates high-quality data integration while minimizing user interaction.

Constraints als Schlüssel zu industriellen Konfigurator-Lösungen

ZusammenfassungModerne industriele Produkte und anlagen sollen viele Leistungsmerkmale bei niedrigen Produktionskosten bieten. Aus einer Menge von Komponenten werden variantenreiche individuelle Anlagen konfiguriert. Konfiguratoren mit Wissensbasen mit Constraints erfüllen diese Anforderungen, was mit großen industriellen Anwendungen gezeigt wurde.AbstractUp-to-date products and systems in industry want to offer many features at low production cost. Using a toolbox of components, individual systems with many variants are configured. Configurators using knowledge-bases with constraints fulfil those requirements. This has been shown with large industry applications.

Combining Prediction Methods for Hardware Asset Management.

As wrong estimations in hardware asset management may cause serious cost issues for industrial systems, a precise and efficient method for asset prediction is required. We present two complementary methods for forecasting the number of assets needed for systems with long lifetimes: (i) iteratively learning a well-fitted statistical model from installed systems to predict assets for planned systems, and using this regression model (ii) providing a stochastic model to estimate the number of asset replacements needed in the next years for existing and planned systems. Both methods were validated by experiments in the domain of rail automation.