Learning in multi-agent systems: Theoretical issues

Abstract

Learning from experience is a powerful technique used by humans to improve their problem-solving ability. It is considered to be an inseparable part of intelligence. Since the ability of learning is fundamental for every intelligent system that should improve its problem solving, it should become a key element in design and development of distributed artificial intelligence (DAI) systems.

Learning in a distributed system can be divided into two parts, namely learning of individual agents and learning of the system as a whole. Learning of individual agents stems from classical methods of machine learning and involves acquisition of new knowledge, development of abilities for problem solving based on previous experience (using methods of inductive learning, learning from analogy). In this way the activity and problem-solving process of individual agents can be improved. However, it does not mean that the behaviour of the whole distributed system must necessarily improve. Satisfying this requirement may be reached by application of learning methods to the system as a whole. The paper focuses on basic issues of learning in multi-agent systems. Learning of individual agents and of the system as a whole is discussed. There is suggested possible architecture for incorporating learning features into the distributed system DISCIM and simulation experiments for evaluation of the methodology.