机器人通过执行经验不断优化任务规划知识，提升成功率。
Experience-based Refinement of Task Planning Knowledge in Autonomous Robots
- 用实际执行中的经验自动修正环境符号知识
- 知识优化后任务失败率随时间持续下降
- 适用于需自主适应复杂环境的实体机器人
要求自主机器人在不断变化的环境中具备高层次认知能力，进行规划与适应，是人工智能领域的一大挑战。尽管自动化规划领域在修复和改进代理在不完整或动态环境模型下的符号知识方面已取得进展，但这些成果尚未应用于真实物理机器人。本文展示了一种物理机器人如何利用行动执行中的经验来驱动知识的自我修正，从而提升其生成任务计划的成功率。为构建更鲁棒的规划系统，我们提出一种域知识精炼方法，以改进智能机器人行为所依赖的知识基础。该架构已在NAO机器人上实现并评估，结果显示，随着错误知识被消除或调整，未来任务计划的失败率持续降低。
原文摘要 · Abstract (English)
The requirement for autonomous robots to exhibit higher-level cognitive skills by planning and adapting in an ever-changing environment is indeed a great challenge for the AI community. Progress has been made in the automated planning community on refinement and repair of an agent's symbolic knowledge to do task planning in an incomplete or changing environmental model, but these advances up to now have not been transferred to real physical robots. This paper demonstrates how a physical robot can be capable of adapting its symbolic knowledge of the environment, by using experiences in robot action execution to drive knowledge refinement and hence to improve the success rate of the task plans the robot creates. To implement more robust planning systems, we propose a method for refining domain knowledge to improve the knowledge on which intelligent robot behavior is based. This architecture has been implemented and evaluated using a NAO robot. The refined knowledge leads to the future synthesis of task plans which demonstrate decreasing rates of failure over time as faulty knowledge is removed or adjusted.
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