Learning service recommendations

2015

J Internet Serv Appl

Self Cite

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.

Section: Markov Model Based On Composition Rulesmentioning

confidence: 99%

An approach towards adaptive service composition in markets of composed services

2015

J Internet Serv Appl

Self Cite

“…A comprehensive formal description was already introduced in our previous work [12]. Figure 5 depicts the graphical representation of the state space belonging to a MDP based on the composition rule system given by (1).…”

Section: Markov Decision Process Modelmentioning

confidence: 99%

“…In contrast, the SARSA algorithm always considers the actual agent's behavior. We already presented quantitative results for applying QLearning based on a pre-defined composition rule set in our previous work [12].…”

Section: E Incorporating Temporal Difference Learningmentioning

confidence: 99%

See 1 more Smart Citation

Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services

2014 IEEE World Congress on Services

Kleinjohann

2014

Self Cite

Automatic service composition is still a challenging task. It is even more challenging when dealing with a dynamic market of services for end users. New services may enter the market while other services are completely removed. Furthermore, end users are typically no experts in the domain in which they formulate a request. As a consequence, ambiguous user requests will inevitably emerge and have to be taken into account. To meet these challenges, we propose a new approach that combines automatic service composition with adaptive service recommendation. A best first backward search algorithm produces solutions that are functional correct with respect to user requests. An adaptive recommendation system supports the search algorithm in decision-making. Reinforcement Learning techniques enable the system to adjust its recommendation strategy over time based on user ratings. The integrated approach is described on a conceptional level and demonstrated by means of an illustrative example from the image processing domain.

“…In our previous work [10], we already presented a service recommendation system, which adapts to concrete requests by means of Reinforcement Learning. In the paper at hand, we introduce the integration of the recommendation system into planning based service composition.…”

Section: Introductionmentioning

confidence: 99%

Applying Reinforcement Learning for Resolving Ambiguity in Service Composition

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

Kleinjohann

2014

Self Cite

Automatically composing service-based software solutions is still a challenging task. Functional as well as nonfunctional properties have to be considered in order to satisfy individual user requests. Regarding non-functional properties, the composition process can be modeled as optimization problem and solved accordingly. Functional properties, in turn, can be described by means of a formal specification language. Statespace based planning approaches can then be applied to solve the underlying composition problem. However, depending on the expressiveness of the applied formalism and the completeness of the functional descriptions, formally equivalent services may still differ with respect to their implemented functionality. As a consequence, the most appropriate solution for a desired functionality can hardly be determined without considering additional information. In this paper, we demonstrate how to overcome this lack of information by means of Reinforcement Learning. In order to resolve ambiguity, we expand state-space based service composition by a recommendation mechanism that supports decision-making beyond formal specifications. The recommendation mechanism adjusts its recommendation strategy based on feedback from previous composition runs. Image processing serves as case study. Experimental results show the benefit of our proposed solution.