Generating inspiration for agent design by reinforcement learning

One major challenge in developing multiagent systems is to find the appropriate agent design that is able to generate the intended overall dynamics, but does not contain unnecessary features. In this article we suggest to use agent learning for supporting the development of an agent model during an analysis phase in agent-based software engineering. , the designer defines the environmental model and the agent interfaces. A reward function captures a description of the overall agent performance with respect to the intended outcome of the agent behavior. Based on this setup, reinforcement learning techniques can be used for learning rules that are optimally governing the agent behavior. However, for really being useful for analysis, the human developer must be able to review and fully understand the learnt behavior program. We propose to use additional learning mechanisms for a post-processing step supporting the usage of the learnt model.