Incremental dynamic programming for on-line adaptive optimal control
Incremental dynamic programming for on-line adaptive optimal control
Neuro-Dynamic Programming
Learning to Predict by the Methods of Temporal Differences
Machine Learning
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning to act using real-time dynamic programming
Artificial Intelligence
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In this paper, we propose a new solution to Reactive Web Service Composition, via molding with Reinforcement Learning, and introducing modified (alterable) QoS variables into the model as elements in the Markov Decision Process tuple. Moreover, we give an example of Reactive-WSC-based mobile banking, to demonstrate the intrinsic capability of the solution in question of obtaining the optimized service composition, characterized by (alterable) target QoS variable sets with optimized values. Consequently, we come to the conclusion that the solution has decent potentials in boosting customer experiences and qualities of services in Web Services, and those in applications in the whole electronic commerce and business sector.