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Reasoning with preferences in service robots

机译:在服务机器人的偏好推理

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摘要

Service Robots should be able to reason about preferences when assisting people in common daily tasks. This functionality is useful, for instance, to respond to action directives that conflict with the user's interest or wellbeing or when commands are underspecified. Preferences are defeasible knowledge as they can change with time or context, and should be stored in a non-monotonic knowledge-base system, capable of expressing incomplete knowledge, updating defaults and exceptions dynamically, and handling multiple extensions. In this paper a knowledge-base system with such an expressive power is presented. Non-monotonicity is handled using a generalization of the Principle of Specificity, which states that in case of knowledge conflict the most specific proposition should be preferred. Reasoning about preferences is used on demand through conversational protocols that are generic and domain independent. We describe the general principles underlying such protocols and their implementation through the SitLog programming language. We also show a demonstration scenario in which the robot Golem-III assists human users using such protocols and preferences stored in its non-monotonic knowledege-base service.
机译:服务机器人应该能够在协助普通的日常任务中时推理偏好。例如,此功能非常有用,以响应与用户的兴趣或幸福或命令不分指定时冲突的操作指令。偏好是可判定的知识,因为它们可以随时间或上下文而变化,并且应该存储在非单调知识库系统中,能够动态地表达不完整的知识,更新默认值和异常,并处理多个扩展。在本文中,提出了一种具有这种表现力的知识库系统。使用特异性原则的概括处理非单调性,这使得在知识冲突的情况下,最具体的命题是最优选的。关于偏好的推理是根据泛型和域独立的对话协议使用的。我们通过Sitlog编程语言描述了诸如此类议定书的普遍原则及其实施。我们还显示了一个示范场景,其中机器人Golem-III协助人类用户使用存储在其非单调知识库基础服务中的这种协议和偏好。

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