Problems of Learning in Multi-Agent Systems

Multi-agent systems are usually very complex in their structure and functionality. In most of the application tasks, it is difficult or sometimes impossible to determine exactly and correctly behavior and activities of a multi-agent system during its design. Therefore it is important to find a way how to improve system's activity during its operation. This can be achieved by learning agents which modify their behaviour according to their experience. There have to be studied and developed new methods of machine learning which will prove useful for this purpose. The paper reviews the basic problems of learning in multi-agent systems and some approaches applied for their solution