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Decision theory made tractable: The value of deliberation, with applications to Markov decision process planning.

机译:决策理论变得易于处理:审议的价值,及其在马尔可夫决策过程规划中的应用。

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摘要

This dissertation addresses the construction of an operational definition of rationality in the face of computational constraints. Rational decision problem classes are often NP hard, almost certainly eliminating the possibility of any agent ever exhibiting behavior that fully meets the decision-theoretic characterization of rationality.;The most promising approaches extant in the artificial intelligence literature, bounded optimality and metalevel control of computational expenditures, do provide insight into possible agent architectures capable of exhibiting many interesting behaviors that have parallels in human problem-solving. They do not, however, resolve the fundamental difficulty of how rationality can be redefined so as to take into account the costs of its own application. This work argues that the role of rationality is in the evaluation of the agent from an external perspective, rather than in the generation of decisions by the agent, providing a new conception of the role of background or situation in decision making. It clarifies what rational metalevel controllers can accomplish and how they should be designed.;Rational metalevel control allows for problem-solving strategies to be much more responsive to a variety of resource constraints and environmental factors. This work presents metalevel architectures for problem domains modellable as Markov decision processes. They are demonstrated to exhibit the desired responsiveness on some simple examples such as mazes. They cope well with time pressure and random environmental variations. They exhibit behaviors that take account of their previous planning efforts, such as sticking to known solutions when thinking is expensive. These features offer new hope for scaling algorithmic stochastic planning to large domains.;Also discussed are methods of abstracting a decision model by coarse graining its state space, and the loss to decision model quality incurred by such an approximation. This work concludes with a discussion of issues that arise when abstract actions are characterized as plans to plan.
机译:本文针对面对计算约束的合理性的操作定义的构建。理性决策问题类别通常是NP难题,几乎可以肯定地消除了任何主体表现出完全符合理性决策理论特征的行为的可能性;人工智能文献中存在的最有前途的方法,计算的有界最优性和元级控制的支出,确实提供了对可能的代理架构的洞察力,这些代理架构能够展示许多有趣的行为,这些行为在解决人类问题方面具有相似性。但是,它们没有解决如何重新定义合理性以考虑其自身应用成本的根本困难。这项工作认为,合理性的作用是从外部角度评估代理人,而不是代理人做出决策,从而提供了背景或情况在决策中的作用的新概念。它阐明了理性的元级别控制器可以完成哪些工作以及应如何设计它们。合理的元级别控制使问题解决策略对各种资源约束和环境因素的响应更加迅速。这项工作提出了可建模为马尔可夫决策过程的问题域的元级体系结构。在一些简单的示例(如迷宫)上,它们表现出所需的响应能力。它们可以很好地应对时间压力和随机环境变化。他们表现出的行为要考虑到他们先前的计划工作,例如在思维成本很高时坚持使用已知的解决方案。这些功能为将算法随机规划扩展到大域提供了新的希望。;还讨论了通过粗化其状态空间来粗化决策模型的方法,以及由此近似所导致的决策模型质量的损失。这项工作的结尾是对抽象行动被描述为计划计划时出现的问题的讨论。

著录项

  • 作者

    Tash, Jonathan King.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Computer Science.;Philosophy.;Operations Research.
  • 学位 Ph.D.
  • 年度 1996
  • 页码 111 p.
  • 总页数 111
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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