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Goal driven integrated intelligence for autonomous systems intelligence planifiée pour les systèmes autonomes

Goal driven integrated intelligence for autonomous systems intelligence planifiée pour les systèmes autonomes
目标驱动的自主系统集成智能 自主系统智能规划
批准号:
155486-2006
负责人:
Kabanza, Froduald
金额:
$2.4万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2007
资助国家:
加拿大
项目状态:
已结题
起止时间:
2007-01-01 至 2008-12-31

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中文摘要
翻译
开发自主系统--无论是硬件(例如机器人)还是软件(例如计算机辅助教育工具)--能够处理尚未明确编程的新情况,仍然是一项复杂和具有挑战性的任务。目前的大多数工具和方法充其量也只是处于实验阶段。对意想不到的事件作出反应、规划和监测完成任务的步骤以及从经验中学习,这些都是实质性的问题,可能会因为系统在感知环境或对环境采取行动方面的内在局限性而变得更加复杂。该项目的一个目标是为复杂的机器人过程开发新的自动化规划算法,这些算法超出了当今规划系统的能力范围,并将这些算法与其他机器人决策过程相结合。这将使机器人能够完成目前方法难以完成的任务,如帮助人们进行日常生活活动、进行手术或检查和修复空间站。另一个目标是为教学对话开发新的规划算法,这是目前的方法不可能的,并将这些算法集成到模拟器中,以支持机器人操作过程的学习和控制。这将提高机器人操作的安全性,并降低培训成本(模拟比实际的机器人设备便宜,而且没有受伤或设备损坏的风险)。这些模拟器可以随时随地安装,以提供“即时”培训,以支持操作员保持其技能水平所需的继续教育(例如,在长时间任务期间)。这个项目主要针对机器人的应用领域,但研究成果在许多其他领域也有潜在的应用。
英文摘要
Developing autonomous systems--whether hardware (e.g., robots) or software (e.g., computer-aided education tools)--that can handle novel situations for which they have not been explicitly programmed remains a complex and challenging task. Most of the current tools and methodologies are, at best, in the experimental stage. Reacting to unanticipated events, planning and monitoring the steps to accomplish a task, and learning from experience all remain substantial problems that can be further complicated by the system's intrinsic limitations in perceiving or acting on its environment. One objective of this project is to develop new automated planning algorithms for complex robotics processes that are beyond the capability of today's planning systems and to integrate these algorithms with other robot decision-making processes. This will enable robots to accomplish tasks that are difficult with current approaches, such as assisting people in everyday life activities, performing surgery or inspecting and repairing space stations. Another objective is to develop new planning algorithms for tutorial dialogues that are impossible with current approaches and to integrate these algorithms into simulators to support the learning and control of robot manipulation procedures. This will increase the safety of robot manipulations and reduce training costs (simulations are less expensive than the actual robotics equipment and there is no risk of injury or equipment damage). These simulators can be installed anywhere and anytime to provide "just-in-time" training to support continuing education necessary for operators to maintain their skill levels (e.g. during long missions). This project primarily targets the application domain of robotics, but the research results have potential applications in many others.
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Advanced Algorithms for Plan Recognition
  • 批准号:
    RGPIN-2017-06350
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.48万
  • 财政年份:
    2022
  • 负责人:
    Kabanza, Froduald
  • 依托单位:
Advanced Algorithms for Plan Recognition
  • 批准号:
    RGPIN-2017-06350
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.48万
  • 财政年份:
    2021
  • 负责人:
    Kabanza, Froduald
  • 依托单位:
Advanced Algorithms for Plan Recognition
  • 批准号:
    RGPIN-2017-06350
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.48万
  • 财政年份:
    2020
  • 负责人:
    Kabanza, Froduald
  • 依托单位:
Advanced Algorithms for Plan Recognition
  • 批准号:
    RGPIN-2017-06350
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.48万
  • 财政年份:
    2019
  • 负责人:
    Kabanza, Froduald
  • 依托单位:
国内基金
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