Efficient Methods for Multi-Objective Decision-Theoretic Planning
Efficient Methods for Multi-Objective Decision-Theoretic Planning
复制标题
多目标决策理论规划的有效方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
D. Roijers
中科院分区：
文献类型：
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作者：
D. Roijers
在决策理论规划问题中，例如（部分可观察的）马尔可夫决策问题或协调图，智能体通常旨在优化标量值函数。然而，在许多现实世界的问题中，智能体面临着多个可能相互冲突的目标。在此类多目标问题中，值是向量而不是标量，我们需要计算覆盖集的方法，即针对目标之间所有可能权衡的一组最佳解决方案。在该项目中，提出了新的多目标规划方法，用于计算所谓的凸覆盖集（CCS）：当政策可以是随机的或偏好是线性时的覆盖集。我们证明 CCS 具有良好的数学特性，并且通常比帕累托前沿更容易计算，帕累托前沿通常被公理地假设为多目标决策问题的解集
In decision-theoretic planning problems, such as (partially observable) Markov decision problems or coordination graphs, agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives. In such multi-objective problems, the value is a vector rather than a scalar, and we need methods that compute a coverage set, i.e., a set of solutions optimal for all possible trade-offs between the objectives. In this project propose new multi-objective planning methods that compute the so-called convex coverage set (CCS): the coverage set for when policies can be stochastic, or the preferences are linear. We show that the CCS has favorable mathematical properties, and is typically much easier to compute that the Pareto front, which is often axiomatically assumed as the solution set for multi-objective decision problems