Alexander Felfernig

Consistency-based diagnosis of configuration knowledge bases

Configuration problems are a thriving application area for declarative knowledge representation that currently experiences a constant increase in size and complexity of knowledge bases. Automated support of the debugging of such knowledge bases is a necessary prerequisite for effective development of configurators. We show that this task can be achieved by consistency-based diagnosis techniques. Based on the formal definition of consistency-based configuration we develop a framework suitable for diagnosing configuration knowledge bases. During the test phase of configurators, valid and invalid examples are used to test the correctness of the system. In case such examples lead to unintended results, debugging of the knowledge base is initiated. Starting from a clear definition of diagnosis in the configuration domain we develop an algorithm based on conflicts. Our framework is general enough for its adaptation to diagnosing customer requirements to identify unachievable conditions during configuration sessions.

Constraint-based recommender systems: technologies and research issues

Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.

UML AS DOMAIN SPECIFIC LANGUAGE FOR THE CONSTRUCTION OF KNOWLEDGE-BASED CONFIGURATION SYSTEMS

In many domains, software development has to meet the challenges of developing highly adaptable software very rapidly. In order to accomplish this task, domain specific, formal description languages and knowledge-based systems are employed. From the viewpoint of the industrial software development process, it is important to integrate the construction and maintenance of these systems into standard software engineering processes. In addition, the descriptions should be comprehensible for the domain experts in order to facilitate the review process. For the realization of product configuration systems, we show how these requirements can be met by using a standard design language (UML-Unified Modeling Language) as notation in order to simplify the construction of a logic-based description of the domain knowledge. We show how classical description concepts for expressing configuration knowledge can be introduced into UML and be translated into logical sentences automatically. These sentences are exploited by a general inference engine solving the configuration task.

Conceptual modeling for configuration of mass-customizable products

Abstract The development and maintenance of product configuration systems is faced with increasing challenges caused by the growing complexity of the underlying knowledge bases. Effective knowledge acquisition is needed since the product and the corresponding configuration system have to be developed in parallel. In this paper, we show how to employ a standard design language (Unified Modeling Language, UML) for modeling configuration knowledge bases. The two constituent parts of the configuration model are the component model and a set of corresponding functional architectures defining which requirements can be imposed on the product. The conceptual configuration model is automatically translated into an executable logic representation. Using this representation we show how to employ model-based diagnosis techniques for debugging faulty configuration knowledge bases, detecting infeasible requirements, and for reconfiguring old configurations.

An Integrated Environment for the Development of Knowledge-Based Recommender Applications

The complexity of the product assortments offered by on-line selling platforms makes selection a challenging task. Customers differ in respect to expertise and product knowledge, but intelligent recommender systems offer personalized dialogues that support the product-selection process. This paper describes CWAdvisor, a domain-independent, knowledge-based recommender environment that provides users with consistently appropriate solutions, identifies additional selling opportunities, and explains solutions. The discussion uses examples from several application domains to show how model-based diagnosis, personalization, and intuitive knowledge-acquisition techniques support customer-oriented sales dialogues. Experience obtained in industrial projects is reported, and successfully deployed recommender applications are evaluated.

A framework for the development of personalized, distributed web-based configuration systems

For the last two decades, configuration systems relying on AI techniques have successfully been applied in industrial environments. These systems support the configuration of complex products and services in shorter time with fewer errors and, therefore, reduce the costs of a mass-customization business model. The European Union-funded project entitled CUSTOMER-ADAPTIVE WEB INTERFACE FOR THE CONFIGURATION OF PRODUCTS AND SERVICES WITH MULTIPLE SUPPLIERS (CAWICOMS) aims at the next generation of web-based configuration applications that cope with two challenges of todays open, networked economy: (1) the support for heterogeneous user groups in an open-market environment and (2) the integration of configurable subproducts provided by specialized suppliers.This article describes the CAWICOMS WORKBENCH for the development of configuration services, offering personalized user interaction as well as distributed configuration of products and services in a supply chain. The developed tools and techniques rely on a harmonized knowledge representation and knowledge-acquisition mechanism, open XML-based protocols, and advanced personalization and distributed reasoning techniques. We exploited the workbench based on the real-world business scenario of distributed configuration of services in the domain of information processing-based virtual private networks.

An efficient diagnosis algorithm for inconsistent constraint sets

Abstract Constraint sets can become inconsistent in different contexts. For example, during a configuration session the set of customer requirements can become inconsistent with the configuration knowledge base. Another example is the engineering phase of a configuration knowledge base where the underlying constraints can become inconsistent with a set of test cases. In such situations we are in the need of techniques that support the identification of minimal sets of faulty constraints that have to be deleted in order to restore consistency. In this paper we introduce a divide and conquer-based diagnosis algorithm (FastDiag) that identifies minimal sets of faulty constraints in an overconstrained problem. This algorithm is specifically applicable in scenarios where the efficient identification of leading (preferred) diagnoses is crucial. We compare the performance of FastDiag with the conflict-directed calculation of hitting sets and present an in-depth performance analysis that shows the advantages of our approach.

Recommender Systems: An Overview

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.

Configuration knowledge representations for Semantic Web applications

Todays economy exhibits a growing trend toward highly specialized solution providers cooperatively offering configurable products and services to their customers. This paradigm shift requires the extension of current standalone configuration technology with capabilities of knowledge sharing and distributed problem solving. In this context a standardized configuration knowledge representation language with formal semantics is needed in order to support knowledge interchange between different configuration environments. Languages such as Ontology Inference Layer (OIL) and DARPA Agent Markup Language (DAML+OIL) are based on such formal semantics (description logic) and are very popular for knowledge representation in the Semantic Web. In this paper we analyze the applicability of those languages with respect to configuration knowledge representation and discuss additional demands on expressivity. For joint configuration problem solving it is necessary to agree on a common problem definition. Therefore, we give a description logic based definition of a configuration problem and show its equivalence with existing consistency-based definitions, thus joining the two major streams in knowledge-based configuration (description logics and predicate logic/constraint based configuration).

Guest Editors' Introduction: Recommender Systems

This special issue presents eight articles, five long and three short, on techniques to improve recommender systems. They cover improving such aspects as user interaction with recommenders, the quality of results presented to users, and user trust in presented recommendations. This article is part of a special issue on Recommender Systems.