Learning Action Sequences Through Imitation in Behavior Based Architectures
In this paper a new architecture for learning action sequences through imitation is proposed. Imitation occurs by means of observing and applying sequences of basic behaviors. When an agent has observed another agent and applied the observed action sequence later on, this imitated action sequence can be seen as a meme. Agents that behave similarly can therefore be grouped by their typical behavioral patterns. This paper thus explores imitation from the view of memetic proliferation.
Combining imitation learning with meme theory we show by simulating agent societies that with imitation significant performance improvements can be achieved. The performance is quantified by using an entropy measure to qualitatively evaluating the emerging clusters.
Our approach is demonstrated by the example of a society of emotion driven agents that imitate each other to reach pleasant emotional state.
KeywordsEmotional State Episode Memory Behavior System Action Sequence Agent Society
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