Andreas A. Falkner

Recommendation Technologies for Configurable Products

State of the art recommender systems support users in the selection of items from a predefined assortment (for example, movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre¬sented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to non-confi¬gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research.

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.

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.

SIEMENS: Configuration and Reconfiguration in Industry

Whereas the configuration of consumer products such as PCs, cars, insurances, and such is well understood and supported by commercial tools, large-scale industrial systems still raise considerable challenges concerning modeling, solving, and performance. After a short explanation of the importance of this topic to Siemens, this chapter presents the domain of railway interlocking systems as an example of such complex industrial systems and discusses detailed requirements for their configuration. It then reports on techniques used for solving those requirements at Siemens as well as on results of their application.

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.

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.

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.