IGERT: A Unified Approach to Sequential Decision-Making in Cognitive Science

这个IGERT项目考察了顺序决策问题，作为研究认知科学中几个中心主题的统一框架：选择性注意、导航、语言处理和多智能体群体中的行动协调。我们的学生接受培训要调查的首要问题是：代理人如何可能决定采取什么行动来实现长期目标？我们认识到，复杂环境中的决策是一个连续的过程，涉及一系列事件，在这些事件中，代理人根据通过其感官获得并存储在内存中的信息，选择适合其目标的行动。由于感官限制和环境复杂性引起的感知不确定性、对可用行动的巨大空间进行分类的挑战以及对行动的长期后果的反馈的固有延迟，这一问题变得困难起来。各种各样的基本认知任务可以归结为顺序决策问题。了解如何解决这些问题将是生物体智能行为一般理论的关键组成部分，也将是设计真正智能机器的关键。为了研究这些问题，我们采用了比较的方法，结合了来自一系列模型系统的见解，包括人类、非人类动物、机器人和智能软件代理。这个多学科框架将使学生能够整合来自不同领域的想法和方法，这些领域一直关注顺序决策的研究(心理学、行为生物学、语言学和计算机科学)，但到目前为止，这些领域基本上是分开的。该培训计划旨在培养受过这种创新、多学科方法培训的新一代科学家。研究生培训将侧重于基础学科教育，这是一套共同的课程，专注于顺序决策框架，并强调有指导的跨学科研究活动，跨越每个学生的整个研究生课程。IGERT是一个NSF范围的项目，旨在应对培养具有多学科背景的博士科学家和工程师的挑战，以及未来职业需求所需的技术、专业和个人技能。该项目旨在通过建立新的、创新的研究生教育和培训模式，在一个超越传统学科界限的合作研究的肥沃环境中，催化研究生教育的文化变革。在该计划的第四年，22个机构被授予奖项，这些机构的项目共同涵盖了NSF支持的所有科学和工程领域。这一具体奖项的知识焦点位于社会、行为和经济科学理事会；计算机和信息科学与工程理事会；工程理事会；生物科学理事会；以及教育和人力资源理事会。

This IGERT project examines the problem of sequential decision-making as a unifying framework for the study of several central topics in cognitive science: selective attention, navigation, language processing, and the coordination of action in multiple-agent groups. The overarching question our students are trained to investigate is the following: how is it possible for an agent to decide what actions to take to achieve long-term goals? We recognize that decision-making in complex environments is a sequential process, involving a series of episodes in which an agent, based on information available through its senses and stored in memory, selects the action appropriate for its goals. The problem is made difficult by perceptual uncertainty arising from sensory limitations and environmental complexity, by the challenge of sorting through the large space of actions available, and by inherent delays in feedback about the long-term consequences of actions. A wide variety of fundamental cognitive tasks can be cast as sequential decision-making problems. Understanding how such problems may be solved will be a critical component of a general theory of intelligent behavior in organisms, and will be essential for the design of truly intelligent machines. To study these problems, we adopt a comparative approach, combining insights from a range of model systems, including humans, non-human animals, robots, and intelligent software agents. This multidisciplinary framework will enable students to integrate ideas and methods from different fields that have been concerned with the study of sequential decision-making (psychology, behavioral biology, linguistics, and computer science), but that have so far remained largely separate. The training program is designed to create a new generation of scientists trained in this innovative, multidisciplinary approach. Graduate training will be focused on fundamental disciplinary education, a common set of courses focused on the sequential decision-making framework, and a strong emphasis on mentored, interdisciplinary research activities that span each student's entire graduate program.IGERT is an NSF-wide program intended to meet the challenges of educating Ph.D. scientists and engineers with the multidisciplinary backgrounds and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing new, innovative models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In the fourth year of the program, awards are being made to twenty-two institutions for programs that collectively span all areas of science and engineering supported by NSF. The intellectual foci of this specific award reside in the Directorates for Social, Behavioral, and Economic Sciences; Computer and Information Science and Engineering; Engineering; Biological Sciences; and Education and Human Resources.