Abstract
This paper investigates the effectiveness of an emergent problem solving method which introduces the characteristics of multiagent learning analyzed from the viewpoint of both organizational learning (OL) in social science and genetics-based machine learning (GBML). A careful investigation of the above method has revealed the following implications: (1) there are two levels in the learning mechanisms of multiagent learning (the indivictual and organizational level) and each level is divided into two types (single- and double-loop learning). The integration of these four learning mechanisms improves the collective performance (good solution with less computational cost) in multiagent environments; (2) the effectiveness of the emergent problem solving in multiagent environments is supported by the following three properties: (a) different dimension in learning mechanisms, (b) meta-level interaction in addition to the interaction among agents, and (c) a combination of exploration at an individual level andt exploitation at an organizational level.
