Episodic memory for autonomous agents

Knowledge about already experienced situations improves the quality of decision making. The outcomes of decisions in the past can be evaluated and used as a feedback mechanism. This enables the decision making unit is able to learn from past situations. In neuro-psychanalysis, this kind of data storage is called episodic memory. This work describes an episodic memory architecture derived from theories from psychology and neuro-psychoanalysis. The intended applications for this architecture are autonomous agents and building automation systems. The results of the simulation with the autonomous agents have shown that agents with the episodic memory adapt faster to new situations than memory-less agents.