{"date_created":"2021-09-29T09:39:32Z","user_id":"21240","citation":{"apa":"Jungmann, A., &#38; Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. <i>Journal of Internet Services and Applications 6(1)</i>, 1–18.","ieee":"A. Jungmann and F. Mohr, “An approach towards adaptive service composition in markets of composed services,” <i>Journal of Internet Services and Applications 6(1)</i>, pp. 1–18, 2015.","ama":"Jungmann A, Mohr F. An approach towards adaptive service composition in markets of composed services. <i>Journal of Internet Services and Applications 6(1)</i>. Published online 2015:1-18.","mla":"Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” <i>Journal of Internet Services and Applications 6(1)</i>, 2015, pp. 1–18.","chicago":"Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” <i>Journal of Internet Services and Applications 6(1)</i>, 2015, 1–18.","short":"A. Jungmann, F. Mohr, 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} }"},"_id":"25107","title":"An approach towards adaptive service composition in markets of composed services","department":[{"_id":"672"}],"author":[{"full_name":"Jungmann, Alexander","last_name":"Jungmann","first_name":"Alexander"},{"first_name":"Felix","last_name":"Mohr","full_name":"Mohr, Felix"}],"abstract":[{"text":"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.","lang":"eng"}],"year":"2015","status":"public","type":"journal_article","date_updated":"2022-01-06T06:56:51Z","publication":"Journal of Internet Services and Applications 6(1)","page":"1-18","language":[{"iso":"eng"}]}