Representation and Reasoning for Cognitive Personal Robotics

认知个人机器人的表示和推理

• 批准号: DP150103034
• 负责人: Prof Michael Thielscher
• 金额: $ 28.99万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-01-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP150103034
• 关键词: Representation; Reasoning; Cognitive; Personal; Robotics

Robotic systems are becoming increasingly more sophisticated and prevalent. Developing complex and maintainable robot programs to control these systems remains a significant challenge particularly given the diversity of robot platforms and application areas. This project aims to build on advances in problem solving and programming paradigms in Artificial Intelligence, applying them to learning sophisticated robot programs. These techniques have the potential to provide for elaboration tolerance, knowledge/program maintenance and optimisation of performance. This project aims to develop techniques for building sophisticated declarative robot programs. It aims to achieve this by learning procedural robot programs and turning them into maintainable declarative robot programs.

机器人系统正变得越来越复杂和流行。开发复杂的和可维护的机器人程序来控制这些系统仍然是一个重大的挑战，特别是考虑到机器人平台和应用领域的多样性。该项目旨在建立在人工智能中解决问题和编程范式的进步基础上，将其应用于学习复杂的机器人程序。这些技术有可能提供精细公差，知识/程序维护和性能优化。该项目旨在开发用于构建复杂的声明式机器人程序的技术。它的目的是通过学习过程式机器人程序并将其转换为主要的声明式机器人程序来实现这一目标。