Principles of intelligent sensorimotor behavior under informational constraints

信息约束下智能感觉运动行为原理

• 批准号: 2034885
• 负责人: Dieter Vanderelst
• 金额: $ 60.04万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034885&HistoricalAwards=false
• 关键词: Principles; intelligent; sensorimotor; behavior; under

Most people think about learning, problem-solving, tool-use, navigation, and communication when considering animal intelligence. However, this view of intelligence is shifting. Increasingly, researchers recognize that merely interacting with the environment requires animals to solve very complex problems. Many of these problems turn out to be more challenging to solve than what is classically defined as intelligent behavior. For example, consider an insect walking across uneven, unpredictable terrain. Coordinating the movement of six legs is a challenging problem. Interacting with the environment is difficult because the animal does not have complete knowledge about its body or the terrain. Nor does it have much time to gather information. This lack of information is common when animals interact with the world. Currently, we do not have a good idea of how animals deal with this lack of knowledge. Bats are ideal animals in which to study this problem. Their sonar system provides them only with limited information about their surroundings. Despite this, they perform astoundingly coordinated and intelligent behavior. The current project investigates strategies echolocating bats use to overcome the lack of sensory information. The aim is to use robots to model several bat behaviors under conditions where the lack of sensory information is the most severe, for example, during the emergence from caves. Using robots instead of computer simulations allows mimicking the sonar signals received by bats faithfully. By examining strategies bats can use to deal with informational deficiencies, the project aims at understanding how animals more generally can overcome this problem.Animal intelligence is often typified by abilities such as learning, problem-solving, tool use, and other facets of higher-order cognition. However, more recent views on animal intelligence emphasize the ability to adjust behavior to the moment-to-moment contingencies arising in interaction with the physical environment. Coping with the dynamic, unpredictable world is challenging. Most importantly, because action and perception have to proceed in the face of substantial informational deficiency. While acting, animals do not have complete knowledge about the world or their bodies. This project proposes and tests strategies for dealing with informational constraints by building bio-robotic models of bat sonar behavior, allowing for veracious mimicking of sonar signals. In particular, it is proposed that direct mapping of task-specific sensory input to action is a fundamental principle for dealing with informational constraints. Echolocating bats are ideal for understanding animal intelligence under informational constraints because their sonar system only provides limited information about the environment. Nevertheless, echolocating bats perform astounding intelligent behavior in interaction with complex environments. Understanding principles for intelligent sensorimotor control might help to understand the so-called higher cognitive functions in animals. These are not discontinuous with sensorimotor behavior. Indeed, understanding the fundamental principles of animal intelligence (and potentially human intelligence) may require learning about their sensorimotor control origins. As a result, investigating the principles supporting vertebrate sensorimotor intelligence might help explain functions more classically associated with intelligence (e.g., tool use and planning). As such, this proposal aims at contributing to a deeper understanding of animal intelligence and cognition.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大多数人在考虑动物智能时会考虑学习、解决问题、使用工具、导航和交流。然而，这种对智能的看法正在发生变化。越来越多的研究人员认识到，仅仅与环境互动就需要动物解决非常复杂的问题。这些问题中的许多问题比传统意义上的智能行为更难解决。例如，考虑一只昆虫在不平坦、不可预测的地形上行走。协调六条腿的运动是一个具有挑战性的问题。与环境互动是困难的，因为动物对自己的身体或地形没有完整的知识。它也没有太多时间收集信息。当动物与世界互动时，这种信息的缺乏是常见的。目前，我们还不知道动物如何应对这种知识的缺乏。蝙蝠是研究这个问题的理想动物。它们的声纳系统只能为它们提供关于周围环境的有限信息。尽管如此，它们还是表现出惊人的协调和智能行为。目前的项目研究回声定位蝙蝠用来克服感官信息缺乏的策略。其目的是使用机器人来模拟几种蝙蝠行为的条件下，缺乏感官信息是最严重的，例如，在出现从洞穴。使用机器人而不是计算机模拟可以忠实地模仿蝙蝠接收的声纳信号。通过研究蝙蝠可以用来处理信息不足的策略，该项目旨在了解动物如何更普遍地克服这个问题。动物智力通常由学习，解决问题，使用工具和高阶认知的其他方面等能力代表。然而，最近关于动物智力的观点强调了在与物理环境的相互作用中随时调整行为的能力。应对动态的、不可预测的世界是一项挑战。最重要的是，因为行动和感知必须在大量信息缺乏的情况下进行。在行动的时候，动物并没有关于世界或它们身体的完整知识。该项目提出并测试了通过建立蝙蝠声纳行为的生物机器人模型来处理信息约束的策略，从而可以准确地模仿声纳信号。特别是，它提出了任务特定的感官输入动作的直接映射是一个基本原则，用于处理信息的限制。回声定位蝙蝠是在信息限制下理解动物智力的理想选择，因为它们的声纳系统只能提供有限的环境信息。然而，回声定位蝙蝠在与复杂环境的互动中表现出惊人的智能行为。理解智能感觉运动控制的原理可能有助于理解动物所谓的高级认知功能。这些与感觉运动行为是不连续的。事实上，理解动物智能（以及潜在的人类智能）的基本原理可能需要了解它们的感觉运动控制起源。因此，研究支持脊椎动物感觉运动智力的原理可能有助于解释与智力更经典相关的功能（例如，工具的使用和规划）。因此，该奖项旨在促进对动物智力和认知的更深入理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。