Practical Reasoning in Autonomous Agents

自主代理的实用推理

• 批准号: 0080888
• 负责人: John Pollock
• 金额: $ 33.12万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0080888&HistoricalAwards=false
• 关键词: Practical; Reasoning; Autonomous; Agents

AI is approaching the point where it will be possible to build autonomous robotic agents capable of performing human-like tasks without direct human control. Such autonomous agents must be able to plan their activities in the face of incomplete knowledge of their environment. This project aims at understanding how such planning works and building implemented systems that accomplish it. Specifically, this investigation is aimed at the construction of an artificial rational agent capable of engaging in decision-theoretic planning in environments of realistic complexity and unpredictability. The design of a system to do automated planning is one of the traditional goals of artificial intelligence research, and some highly successful planning systems have been constructed for use in narrowly constrained environment; however, these systems presuppose that the planner knows everything it needs to know when it is first presented with the planning problem, and most of them further require complete knowledge of all relevant aspects of the agent's environment and knowledge of precisely what will result from performing any relevant act in any circumstance the planner will encounter. While such assumptions might be satisfied by an industrial robot operating in a constrained environment, human beings plan without satisfying any of these conditions. In particular, planning problems often drives the search for new knowledge rather than presupposing that the planning agent knows everything it needs to know from the beginning. And human beings do not assume that they can predict with certainty what will happen when they perform any available action under any conceivable circumstances. In constructing and evaluating plans, people take account of the varying probabilities of different consequences of actions, and they assign values and costs to those consequences before deciding whether to adopt a proposed plan. In other words, they plan decision-theoretically. The objective of this project is to understand how decision-theoretic planning is possible in an agent operating in an uncooperative and only partially predictable environment, and then to build an artificial agent whose planning capabilities more closely approximate those of human beings. This should illuminate some of the structure of rational cognition in both artificial agents and human agents.

人工智能正在接近这样一个点，即有可能构建能够在没有人类直接控制的情况下执行类似人类任务的自主机器人代理。 这种自主行为者必须能够在对其环境不完全了解的情况下规划其活动。 本项目旨在了解这种规划是如何工作的，并建立实现它的系统。具体来说，本研究旨在构建一个人工理性代理，能够在现实复杂性和不可预测性的环境中进行决策理论规划。自动规划系统的设计是人工智能研究的传统目标之一，并且已经构建了一些非常成功的规划系统用于窄约束环境;然而，这些系统的前提是计划者在第一次遇到计划问题时就知道了它需要知道的一切，而且大多数还需要对行动者所处环境的所有相关方面有完整的了解，并精确地知道在计划者将遇到的任何情况下执行任何相关行动会产生什么结果。 虽然在受限环境中操作的工业机器人可能满足这些假设，但人类在不满足任何这些条件的情况下进行规划。 特别是，规划问题往往会驱动对新知识的搜索，而不是预先假设规划代理从一开始就知道它需要知道的一切。 而且，人类并不认为他们能够肯定地预测，当他们在任何可以想象的情况下采取任何可行的行动时，会发生什么。 在制定和评估计划时，人们会考虑行动不同后果的不同概率，并在决定是否采用拟议的计划之前为这些后果分配价值和成本。 换句话说，他们在理论上计划决策。 这个项目的目标是了解如何决策理论规划是可能的代理在一个不合作的，只有部分可预测的环境，然后建立一个人工代理的规划能力更接近人类。 这应该可以阐明人工智能体和人类智能体的理性认知结构。