A Quantitative Investigation into Distribution of Memory and Learning in Multi Agent Systems with Implicit Communications

In this paper we have investigated a group of multi agent systems (MAS) in which the agents change their environment and this change has the potential to trigger behaviors in other agents of the group in another time or another position in the environment. The structure makes it possible to conceptualize the group as a super organism incorporating the agents and the environment such that new behaviors are observed from the whole group as a result of the specific distribution of agents in that environment. This distribution exists in many aspects like a super memory (or even a super brain) that exists in the environment and is not limited to memories of the individuals. There is a distributed decision making that is done by the group of agents which, in a higher level consists of both individual and group decision makings, and can be viewed as emergent rather than consciously planned. As the agents change the environment, they decrease the error for the group and hence a distributed learning is also forming between the agents of the group. This implicit learning is related to the implicit memory existing in the environment. These two interpretations of memory and learning are assessed with experimental results where two robots perform a task while they are not aware of their global behavior.