LEARNING BY OBSERVATION IN SOFTWARE AGENTS

In a society of similar agents, all a them using the same kind of knowledge representation, learning with others could be achieved through direct transfer of knowledge from experts to apprentices. However, not all agents use the same kind of representation methods, hence learning by direct communication of knowledge is not always possible. In such cases, learning by observation might be of key importance. This paper presents an agent architecture that provides software agents with learning by observation capabilities similar to those observed in superior mammals. The main contribution of our proposal is to let software agents learn by direct observation of the actions being performed by expert agents. This is possible because. using the proposed architecture, agents may see one another.