Maroto-Gómez Marcos, Burguete-Alventosa Javier, Álvarez-Arias Sofía, Malfaz María, Salichs Miguel Ángel
Department of Systems Engineering and Automation, University Carlos III of Madrid, Av. de la Universidad, 30, 28911 Leganes, Madrid, Spain.
Biomimetics (Basel). 2024 Aug 21;9(8):504. doi: 10.3390/biomimetics9080504.
Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model's performance in five scenarios, emphasising how dopamine levels vary depending on the robot's situation and stimuli perception. Moreover, we show the model's integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot.
决策系统允许智能体根据它们从环境和内部过程中感知到的信息来调整其行为。人类拥有独特的决策能力，能够适应当前情况并预见未来挑战。具有模仿人类的适应性和预期性决策能力的自主机器人，可以为机器人赋予用户更容易理解的技能。人类的决策高度依赖多巴胺，这是一种调节动机和奖励的脑物质，它能识别积极和消极的情况。考虑到最近关于多巴胺在人类大脑中的作用及其对决策和动机行为的影响的神经科学研究，本文提出了一个基于多巴胺如何驱动人类动机和决策的模型。该模型允许机器人在动态环境中自主行为，学习最佳行动选择策略并预见未来奖励。结果展示了该模型在五种场景下的性能，强调了多巴胺水平如何根据机器人的情况和刺激感知而变化。此外，我们展示了该模型与迷你社交机器人的集成，以深入了解多巴胺水平如何驱动有动机的自主行为，调节机器人中模拟的受生物启发的内部过程。
