TY - CONF AB - Automatically composing service-based software solutionsis still a challenging task. Functional as well as nonfunctionalproperties have to be considered in order to satisfyindividual user requests. Regarding non-functional properties,the composition process can be modeled as optimization problemand solved accordingly. Functional properties, in turn, can bedescribed by means of a formal specification language. Statespacebased planning approaches can then be applied to solvethe underlying composition problem. However, depending on theexpressiveness of the applied formalism and the completenessof the functional descriptions, formally equivalent services maystill differ with respect to their implemented functionality. As aconsequence, the most appropriate solution for a desired functionalitycan hardly be determined without considering additionalinformation. In this paper, we demonstrate how to overcome thislack of information by means of Reinforcement Learning. Inorder to resolve ambiguity, we expand state-space based servicecomposition by a recommendation mechanism that supportsdecision-making beyond formal specifications. The recommendationmechanism adjusts its recommendation strategy basedon feedback from previous composition runs. Image processingserves as case study. Experimental results show the benefit of ourproposed solution. AU - Jungmann, Alexander AU - Mohr, Felix AU - Kleinjohann, Bernd ID - 457 T2 - Proceedings of the 7th International Conference on Service Oriented Computing and Applications (SOCA) TI - Applying Reinforcement Learning for Resolving Ambiguity in Service Composition ER - TY - CONF AB - Services are self-contained software components that can be used platform independent and that aim at maximizing software reuse. A basic concern in service oriented architectures is to measure the reusability of services. One of the most important qualities is the functional reusability, which indicates how relevant the task is that a service solves. Current metrics for functional reusability of software, however, either require source code analysis or have very little explanatory power. This paper gives a formally described vision statement for the estimation of functional reusability of services and sketches an exemplary reusability metric that is based on the service descriptions. AU - Mohr, Felix ID - 428 T2 - Proceedings of the 12th International Conference on Service Oriented Computing (ICSOC) TI - Estimating Functional Reusability of Services ER - TY - JOUR AU - Agarwal, M. AU - Fallah Tehrani, A. AU - Hüllermeier, Eyke ID - 16046 IS - 3-4 JF - Journal of Multi-Criteria Decision Analysis TI - Preference-based learning of ideal solutions in TOPSIS-like decision models VL - 22 ER - TY - JOUR AU - Krotzky, T. AU - Fober, T. AU - Hüllermeier, Eyke AU - Klebe, G. ID - 16060 IS - 5 JF - IEEE/ACM Transactions of Computational Biology and Bioinformatics TI - Extended graph-based models for enhanced similarity search in Cabase VL - 11 ER - TY - JOUR AU - Hüllermeier, Eyke ID - 16064 IS - 7 JF - International Journal of Approximate Reasoning TI - Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization VL - 55 ER - TY - JOUR AU - Henzgen, Sascha AU - Strickert, M. AU - Hüllermeier, Eyke ID - 16069 JF - Evolving Systems TI - Visualization of evolving fuzzy-rule-based systems VL - 5 ER - TY - JOUR AU - Busa-Fekete, Robert AU - Szörenyi, B. AU - Weng, P. AU - Cheng, W. AU - Hüllermeier, Eyke ID - 16077 IS - 3 JF - Machine Learning TI - Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. VL - 97 ER - TY - JOUR AU - Krempl, G. AU - Zliobaite, I. AU - Brzezinski, D. AU - Hüllermeier, Eyke AU - Last, M. AU - Lemaire, V. AU - Noack, T. AU - Shaker, A. AU - Sievi, S. AU - Spiliopoulou, M. AU - Stefanowski, J. ID - 16078 IS - 1 JF - SIGKDD Explorations TI - Open challenges for data stream mining research VL - 16 ER - TY - JOUR AU - Strickert, M. AU - Bunte, K. AU - Schleif, F.M. AU - Hüllermeier, Eyke ID - 16079 JF - Neurocomputing TI - Correlation-based embedding of pairwise score data VL - 141 ER - TY - JOUR AU - Shaker, Ammar AU - Hüllermeier, Eyke ID - 16080 IS - 1 JF - International Journal of Applied Mathematics and Computer Science TI - Survival analysis on data streams: Analyzing temporal events in dynamically changing environments VL - 24 ER - TY - JOUR AU - Senge, Robin AU - Bösner, S. AU - Dembczynski, K. AU - Haasenritter, J. AU - Hirsch, O. AU - Donner-Banzhoff, N. AU - Hüllermeier, Eyke ID - 16082 JF - Information Sciences TI - Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty VL - 255 ER - TY - JOUR AU - Donner-Banzhoff, N. AU - Haasenritter, J. AU - Hüllermeier, Eyke AU - Viniol, A. AU - Bösner, S. AU - Becker, A. ID - 16083 IS - 67 JF - Journal of Clinical Epidemiology TI - The comprehensive diagnostic study is suggested as a design to model the diagnostic process VL - 2 ER - TY - CONF AU - Busa-Fekete, Robert AU - Szörényi, B. AU - Hüllermeier, Eyke ID - 10247 T2 - Proceedings AAAI 2014, Quebec, Canada TI - PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences ER - TY - CONF AU - Busa-Fekete, Robert AU - Hüllermeier, Eyke ID - 10248 T2 - Proceedings Int. Conf. on Algorithmic Learning Theory (ALT), Bled, Slovenia TI - A Survey of Preference-Based Online Learning with Bandit Algorithms ER - TY - CONF AU - Henzgen, Sascha AU - Hüllermeier, Eyke ID - 10249 T2 - Proceedings Discovery Science, Bled,Slovenia TI - Mining Rank Data ER - TY - CONF AU - Fallah Tehrani, A. AU - Strickert, M. AU - Hüllermeier, Eyke ID - 10250 T2 - Proceedings ESANN , Bruges, Belgium TI - The Choquet kernel for monotone data ER - TY - CONF AU - Abdel-Aziz, A. AU - Strickert, M. AU - Hüllermeier, Eyke ID - 10251 T2 - Proceedings Int. Conf. Case-Based Reasoning (ICCBR), Cork, Ireland TI - Learning Solution Similarity in Preference-Based CBR ER - TY - CONF AU - Schäfer, Dirk AU - Hüllermeier, Eyke ID - 10253 T2 - Proceedings Lernen-Wissensentdeckung-Adaptivität (LWA), Aachen, Germany TI - Dyad Ranking Using A Bilinear Plackett-Luce Model ER - TY - CONF AU - Calders, T. AU - Esposito, F. AU - Hüllermeier, Eyke AU - Meo, R. ID - 10254 T2 - Proceedings, Parts I-III. Lecture Notes in Computer Science TI - Machine Learning and Knowledge Discovery in Databases-European Conf. ECML/PKDD, Nancy, France ER - TY - CONF AU - Fürnkranz, J. AU - Hüllermeier, Eyke AU - Rudin, Cynthia AU - Slowinski, Roman AU - Sanner, Scott ID - 10295 IS - 3 TI - Preference Learning (Dagstuhl Seminar 14101) Dagstuhl Reports VL - 4 ER -