We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form advisor-advisee relationships in order to speed up individual learning and exploit and learn from data on the Web.

In [24], the proposal for an inter-agent communication language (ACL) that gave rise to Java Agent Development Framework (JADE), whose best-known original document is [25] followed by a complementary article [26] and a much more complete text in [27]. The importance of the environment, in which the agents interact, is characterized in a very lucid manner in [28]. All active FIPA specifications are listed in Table I.