EAGER: Enabling Taskability: Research on Teaching Computers new Tasks

EAGER：实现任务能力：教授计算机新任务的研究

• 批准号: 1430613
• 负责人: John Laird
• 金额: $ 29.86万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430613&HistoricalAwards=false
• 关键词: EAGER; Enabling; Taskability; Research; Teaching

This study investigates a new line of research into the "taskability" of cognitive agents. Taskability is the ability of an agent to accept high level, task-oriented instructions from a human and translate those goal-oriented directives into a suitably decomposed plan of sensing, reasoning, or action. Little to no research has been conducted on this subject to date. This research defines taskability in a manner sufficiently formal to allow for scientific research, establishes some of the initial conditions needed to evaluate taskable agents, and advances theories and prototype agents that meet those requirements. Specifically, the four research activities being undertaken in this study include: 1. A review, analysis, and synthesis of prior work from multiple disciplines that that can lay groundwork for focused cognitive systems research in this area; 2) An analysis of the different types of tasks and their structure, as well as the associated types of knowledge that must be learned by taskable agents; 3) Research on how people use instruction with taskable agents for such activities, performing user studies to determine the required task knowledge and agent capabilities; and 4) An extension of current cognitive agent capabilities to support the behaviors observed in the aforementioned studies, resulting in a software system that can be evaluated and co-developed with theory. The current state of the art in research for cognitive systems and agents in general allows human handlers to issue directions to agents either in terms of lower level action primitives that correspond to the agent's particular design, or constrained by a higher-order action language (or even visual repertoire) that is engineered to suffice as a shorthand for some sequence of the same kinds of agent-specific behaviors. Taskability requires that an agent be able to interpret a richer, less constrained set of instructions from a user and, despite a lack of precompiled task decomposition instructions, dynamically formulate an accurate representation of the task to be performed, and even learn new tasks via this sort of interaction. Taskable agents would fundamentally change the way humans interact with intelligent agents and robotic systems in a broad range of disciplines and environments, leading to richer interactions with more capable cars, phones, and almost any device containing a computer, all of which might respond to human interaction without resort to preprogrammed interaction modes. This in turn promises transformational advances in a range of application domains such as health care, industry, government, and home automation.

本研究探讨了一个新的研究线的“任务”的认知代理。可任务性是智能体从人类那里接受高层次的、面向任务的指令，并将这些面向目标的指令翻译成适当分解的感知、推理或行动计划的能力。迄今为止，对这一主题的研究很少或根本没有。 这项研究定义的方式足够正式，以允许科学研究的任务，建立了一些初始条件需要评估任务代理，并提出了理论和原型代理，满足这些要求。 具体而言，本研究所进行的四项研究活动包括：1。回顾、分析和综合多个学科的先前工作，为这一领域的认知系统研究奠定基础; 2）分析不同类型的任务及其结构，以及可分配任务的代理必须学习的相关知识类型; 3）研究人们如何使用具有可任务代理的指令进行此类活动，执行用户研究以确定所需的任务知识和代理能力;以及4）当前认知代理能力的扩展，以支持在上述研究中观察到的行为，从而产生可以与理论一起评估和共同开发的软件系统。 认知系统和智能体研究的当前技术水平一般允许人类处理者根据与智能体特定设计相对应的较低级别动作基元向智能体发出指示，或者受到高阶动作语言（甚至视觉指令集）的约束。该语言被设计为足以作为某些相同类型的智能体特定行为序列的简写。任务能力要求代理能够解释来自用户的更丰富、更少约束的指令集，并且尽管缺乏预编译的任务分解指令，但动态地制定要执行的任务的准确表示，甚至通过这种交互来学习新任务。 任务代理将从根本上改变人类与智能代理和机器人系统在广泛的学科和环境中的交互方式，导致与更强大的汽车，电话和几乎任何包含计算机的设备进行更丰富的交互，所有这些都可以响应人类交互而无需诉诸预编程的交互模式。这反过来又有望在医疗保健、工业、政府和家庭自动化等一系列应用领域取得变革性进展。