[{"_id":"25107","date_updated":"2022-01-06T06:56:51Z","creator":{"login":"wiechers","id":"21240"},"language":[{}],"uri_base":"https://ris.uni-paderborn.de","page":"1-18","citation":{"apa":"Jungmann, A., & Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications 6(1), 1–18.","chicago":"Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” Journal of Internet Services and Applications 6(1), 2015, 1–18.","bibtex":"@article{Jungmann_Mohr_2015, title={An approach towards adaptive service composition in markets of composed services}, journal={Journal of Internet Services and Applications 6(1)}, author={Jungmann, Alexander and Mohr, Felix}, year={2015}, pages={1–18} }","mla":"Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” Journal of Internet Services and Applications 6(1), 2015, pp. 1–18.","short":"A. Jungmann, F. Mohr, Journal of Internet Services and Applications 6(1) (2015) 1–18.","ieee":"A. Jungmann and F. Mohr, “An approach towards adaptive service composition in markets of composed services,” Journal of Internet Services and Applications 6(1), pp. 1–18, 2015."},"type":"journal_article","abstract":[{"lang":"eng"}],"user_id":"21240","dc":{"type":["info:eu-repo/semantics/article","doc-type:article","text","http://purl.org/coar/resource_type/c_6501"],"language":["eng"],"rights":["info:eu-repo/semantics/closedAccess"],"title":["An approach towards adaptive service composition in markets of composed services"],"source":["Jungmann A, Mohr F. An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications 6(1). Published online 2015:1-18."],"creator":["Jungmann, Alexander","Mohr, Felix"],"description":["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."],"date":["2015"],"identifier":["https://ris.uni-paderborn.de/record/25107"]},"department":[{"tree":[{"_id":"7"},{"_id":"34"},{"_id":"44"},{"_id":"43"}],"_id":"672"}],"publication":"Journal of Internet Services and Applications 6(1)","author":[{"first_name":"Alexander","last_name":"Jungmann"},{"last_name":"Mohr","first_name":"Felix"}],"date_created":"2021-09-29T09:39:32Z","status":"public","dini_type":"doc-type:article"}]