ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence

CavSimBase: A database for large scale comparison of protein binding sites - Leinweber, M.; Fober, T.; Strickert, M.; Baumgärtner, L.; Klebe, G.; Freisleben, B.; Hüllermeier, Eyke (IEEE Transactions on Knowledge and Data Engineering)

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification - Riemenschneider, M.; Senge, Robin; Neumann, U.; Hüllermeier, Eyke; Heider, D. (BioData Mining)

Market-Specific Service Compositions: Specification and Matching - Arifulina, Svetlana; Platenius, Marie Christin; Mohr, Felix; Engels, Gregor; Schäfer, Wilhelm (Proceedings of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service Composition for the Future Internet)
The Collaborative Research Centre "On-The-Fly Computing" works on foundations and principles for the vision of the Future Internet. It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management.

An approach towards adaptive service composition in markets of composed services - Jungmann, Alexander; Mohr, Felix (Journal of Internet Services and Applications)
On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques.

A Metric for Functional Reusability of Services - Mohr, Felix (Proceedings of the 14th International Conference on Software Reuse (ICSR))
Services are self-contained software components that can beused platform independent and that aim at maximizing software reuse. Abasic concern in service oriented architectures is to measure the reusabilityof services. One of the most important qualities is the functionalreusability, which indicates how relevant the task is that a service solves.Current metrics for functional reusability of software, however, have verylittle explanatory power and do not accomplish this goal.This paper presents a new approach to estimate the functional reusabilityof services based on their relevance. To this end, it denes the degreeto which a service enables the execution of other services as its contri-bution. Based on the contribution, relevance of services is dened as anestimation for their functional reusability. Explanatory power is obtainedby normalizing relevance values with a reference service. The applicationof the metric to a service test set conrms its supposed capabilities.

Automated Online Service Composition - Mohr, Felix; Jungmann, Alexander; Kleine Büning, Hans (Proceedings of the 12th IEEE International Conference on Services Computing (SCC))
Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.

Fast Fuzzy Pattern Tree Learning for Classification - Senge, Robin; Hüllermeier, Eyke (IEEE Transactions on Fuzzy Systems)

Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations - Schäfer, D.; Hüllermeier, Eyke (in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal)

A cbr approach to the angry birds game - Paul, Adil; Hüllermeier, Eyke (In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany)

Depth estimation in monocular images: Quantitative versus qualitative approaches - Ewerth, R.; Balz, A.; Gehlhaar, J.; Dembczynski, K.; Hüllermeier, Eyke (In Proceedings 25. Workshop Computational Intelligence, Dortmund, Germany)

Locally weighted regression through data imprecisiation - Lu, S.; Hüllermeier, Eyke (in Proceedings 25th Workshop Computational Intelligence, Dortmund Germany)

Fast fuzzy pattern tree learning for classification - Senge, Robin; Hüllermeier, Eyke (IEEE Transactions on Fuzzy Systems)

From knowledge-based to data driven fuzzy modeling: Development, criticism and alternative directions - Hüllermeier, Eyke (Informatik Spektrum)

Does machine learning need fuzzy logic? - Hüllermeier, Eyke (Fuzzy Sets and Systems)

On the Bayes-optimality of F-measure maximizers - Waegeman, W.; Dembczynski, K.; Jachnik, A.; Cheng, W.; Hüllermeier, Eyke (Journal of Machine Learning Research)

Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study - Shaker, A.; Hüllermeier, Eyke (Neurocomputing)

Case-Based Reasoning Research and Development - Hüllermeier, Eyke; Minor, M. (in Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015) LNAI 9343)

Proceedings 25. Workshop Computational Intelligence KIT Scientific Publishing - Hoffmann, F.; Hüllermeier, Eyke

Case Base Maintenance in Preference-Based CBR - Abdel-Aziz, A.; Hüllermeier, Eyke (In Proceedings 23rd International Conference on Case-Based Reasoning (ICCBR 2015))