Research on Real-Time Decision Making: The RALPH Project

本研究的目的是了解 有限的计算资源对设计的影响 最佳决策系统，以便能够建立代理 在面对复杂性的时候， 在真实的时间里。 理论阶段包括完成一个 关于计算推理的规范理论。 的方法 将计算视为行动，其结果是 概率和效用使用决策理论来选择 他们之间 该理论将被纳入一个多层次 决策理论架构，它扩展了 显式元级推理，以递归方式覆盖 决策过程。 该方法已应用 成功地应用到游戏系统中，并将被修改为 适用于机器人运动规划等单智能体问题。 然而，元级推理往往是昂贵的，而且成本可以 只有在将来解决问题时才能摊销， 机制可用。 因此，研究人员将研究 在任何地方发生的推理过程的编译方法 在等级制度中。 他将扩展基于解释的学习 技术，以涵盖选择最佳行动的情况下，其中 概率时间精度折衷起着重要的作用。

The objective of the proposed research is to understand the effect of limited computational resources on the design of optimal decision making sytems, so as to be able to build agents that are robust in the face of complexity, and capable of acting in real time. The theoretical phase involves the completion of a normative theory for reasoning about computations. The approach is to treat computations as actions, with outcomes of varying probabilities and utilities using decision theory to choose between them. The theory will be incorporated in a multilevel decision theoretic architecture, which extends the idea of explicit metalevel reasoning to cover recursively all aspects of the decision making process. The approach has been applied successfully to game playing systems, and will be modified to apply to single agent problems such as robot motion planning. However, metalevel reasoning to often expensive, and the cost can only be amortized over future problem solving if a compilation mechanism is available. The investigator will therefore study methods for compilation of reasoning processes occuring anywhere in the hierarchy. He will extend explanation based learning techniques to cover the case of choosing best actions, where probabilistic time accuracy tradeoffs play a significant role.