SAM: Multi-strategy learning system for decision making on big-data environments.
The main project´s goal is to design and develop an architecture that integrates learning strategies from a cooperative or competitive viewpoint. The proposed architecture adopts a model in which the representation is not defined beforehand but depends on the context and on the concepts involved in the learning process as a result of the evolution of the system while dealing with the problem. The learning system is able to combine perception and representation of a learning problem in such a way that its problem representation is conditioned by its problem perception and at the same time the way it perceives the problem depends on the problem representation. The architecture's dynamics complies with perception-representation-operation cycles, through the interaction among three spaces, each space being responsible for one activity in a cycle: conceptual space, working space and operator space.