Agent-based Adaptation System for Service-oriented Architectures Using Supervised Learning

In this paper we propose an agent-based system for Service-Oriented Architecture self- adaptation. Services are supervised by autonomous agents which are responsible for decid- ing which service should be chosen for interoperation. Agents learn the choice strategy au- tonomously using supervised learning. In experiments we show that supervised learning (Näıve Bayes, C4.5 and Ripper) allows to achieve much better efficiency than simple strategies such as random choice or round robin. What is also important, supervised learning generates a knowledge in a readable form, which may be analyzed by experts.