KDI: Sequential Decision Making in Animals and Machines

KDI：动物和机器的顺序决策

• 批准号: 9873531
• 负责人: John Henderson
• 金额: $ 80万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-01 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9873531&HistoricalAwards=false
• 关键词: KDI; Sequential; Decision; Making; Animals

9873531HendersonMobile organisms make accurate behavioral decisions with extraordinary speed and flexibility in real-world environments despite incomplete knowledge about the state of the world and the effects of their actions. This ability must be shared by artificial agents such as mobile robots if they are to operate flexibly in similar environments. The main goal of the research is to undertake a detailed interdisciplinary study of sequential decision making across animals and robots, with a focus on real time learning and control of information gathering and navigational behaviors.The project will take a comparative approach, combining psychophysical and cognitive research techniques from the study of human eye movement control, behavioral research techniques from the study of insect navigation, and computational methods from the study of mobile robots. All of these systems provide experimentally tractable test-beds for studying real-time decision making in partially observable environments.The research is guided by a class of sequential decision making models called Markov decision processes (MDP). These models are attractive because they provide a formal framework for computing optimal behavior in uncertain environments. However, these models do not fully capture the complexity of decision making in organisms. We will explore extensions of the MDP framework using insights gained from the study of behavior in organisms and algorithms in artificial agents. This synergy will lead both to a better theoretical understanding of sequential decision making in biological organisms, and to the development of efficient algorithms for artificial agents.A major outcome of the project will be to show how the design of artificial creatures (robots) can be guided by, and serve as a guide for, the study of sequential behavior in animals. Understanding the challenges that robot designers face, and the formal framework that they have developed to tackle these challenges, leads to novel questions about organisms behavior. Similarly, insights gained from organisms will help suggest ways for improving algorithms for building intelligent artificial agents.***

9873531 HendersonMobile生物在现实世界的环境中以非凡的速度和灵活性做出准确的行为决策，尽管对世界的状态及其行为的影响的知识不完整。 这种能力必须共享人工代理，如移动的机器人，如果他们在类似的环境中灵活地操作。 该研究的主要目标是对动物和机器人的顺序决策进行详细的跨学科研究，重点是信息收集和导航行为的真实的时间学习和控制。该项目将采取比较方法，结合人类眼动控制研究的心理物理和认知研究技术，昆虫导航研究的行为研究技术，和计算方法从移动的机器人的研究。 所有这些系统都为研究部分可观测环境中的实时决策提供了实验上易于处理的测试平台，研究由一类称为马尔可夫决策过程（MDP）的序列决策模型指导。 这些模型是有吸引力的，因为它们提供了一个正式的框架，计算在不确定的环境中的最佳行为。 然而，这些模型并没有完全捕捉到生物体决策的复杂性。 我们将探索MDP框架的扩展，使用从生物体中的行为和人工代理中的算法的研究中获得的见解。 这种协同作用将导致更好地理解生物有机体中的顺序决策的理论，并为人工agents.A有效的算法的发展该项目的主要成果将是显示人工生物（机器人）的设计如何可以引导，并作为动物顺序行为研究的指导。 了解机器人设计师所面临的挑战，以及他们为应对这些挑战而开发的正式框架，会引发有关生物体行为的新问题。 同样，从生物体中获得的见解将有助于提出改进构建智能人工代理的算法的方法。