Practical Reasoning in Autonomous Agents

人工智能正在接近这样一个点，即有可能建立自动机器人代理，能够在没有直接人类控制的情况下执行类似人类的任务。这种自主代理必须能够在对其环境的不完全了解的情况下计划他们的活动。这个项目旨在了解这种规划是如何工作的，并建立实现它的已实现的系统。具体地说，本研究旨在构建一种能够在现实复杂和不可预测的环境中进行决策理论规划的人工理性代理。设计一个进行自动计划的系统是人工智能研究的传统目标之一，一些非常成功的计划系统已经被构建用于狭隘的环境中；然而，这些系统的前提是计划者在第一次遇到计划问题时知道它需要知道的一切，其中大多数进一步要求完全了解智能体环境的所有相关方面，以及在计划者将遇到的任何情况下执行任何相关行为将导致什么的确切知识。虽然在受限环境中运行的工业机器人可能会满足这些假设，但人类的计划并不满足这些条件中的任何一个。特别是，计划问题经常推动对新知识的搜索，而不是假设计划代理从一开始就知道它需要知道的一切。人类也不会假定他们能够在任何可以想象的情况下，确定地预测当他们执行任何可用行动时会发生什么。在构建和评估计划时，人们会考虑行动的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，他们从理论上规划决策。这个项目的目标是了解决策理论规划如何在不合作且只有部分可预测的环境中操作的代理中可能，然后建立一个规划能力更接近人类的人工代理。这应该可以解释人工代理人和人类代理人的理性认知结构。

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.