Representation and reasoning for autonomous agents

自主代理的表示和推理

• 批准号: 918-2013
• 负责人: Levesque, Hector
• 金额: $ 2.62万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=543918
• 关键词: Representation; reasoning; autonomous; agents

This research concerns the reasoning that a robot would have to do to interact properly with a world that is changing and incompletely known. Almost all the research in robotics today concerns basic level tasks such as dealing with noisy sensors (like sonar), controlling inexact manipulators (like robot arms), or determining how to move safely without colliding into people or obstacles. Higher level decisions, such as when a robot needs to move at all, in what order it should tackle its jobs, or what it should try to do if something goes wrong, usually end up being scripted in advance. The goal of the research proposed here is to investigate what it would take for an automated agent to be able to make such decisions for itself, based on it knowing what it is trying to do, what it knows about its world, and what it has been able to find out using its sensors. To get these decisions to come out of a general reasoning process, however, it will be necessary to express in a language suitable for automated reasoning, enough of what the agent needs to know about its world, goals, actions, ability, the cognitive states of other agents, collaborative task execution, and so on. The proposed research would examine various aspects of these problems, and attempt to develop high level agent controllers of this sort.

这项研究关注的是一个机器人必须做的推理，以正确地与一个不断变化和不完全了解的世界进行互动。今天，几乎所有的机器人研究都涉及基本的任务，比如处理嘈杂的传感器（比如声纳），控制不精确的机械手（比如机器人手臂），或者确定如何安全移动而不撞到人或障碍物。更高层次的决策，比如机器人什么时候需要移动，它应该以什么顺序处理它的工作，或者如果出现问题它应该尝试做什么，通常都是提前编写好的。这里提出的研究目标是调查如何让一个自动代理能够自己做出这样的决定，基于它知道自己在做什么，它对自己的世界有什么了解，以及它使用传感器能够发现什么。然而，为了从一般的推理过程中得出这些决策，有必要用一种适合自动推理的语言来表达智能体需要知道的关于其世界、目标、行动、能力、其他智能体的认知状态、协作任务执行等的足够信息。拟议的研究将检查这些问题的各个方面，并尝试开发这类高级代理控制器。