论文标题
通过在线学习逆多目标优化
Inverse Multiobjective Optimization Through Online Learning
论文作者
论文摘要
我们研究了基于一组依次到达的决策的多目标决策模型的目标功能或约束的问题。特别是，这些决策可能不是确切的，并且可能带有测量噪声，或者是由决策者的有限合理性产生的。在本文中，我们提出了一个一般的在线学习框架，以使用逆多物原理优化来处理这个学习问题。更确切地说，我们开发了具有隐式更新规则的两种在线学习算法，可以处理嘈杂的数据。数值结果表明，这两种算法都可以精确地学习参数，并且对噪声非常强大。
We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and possibly carry measurement noise or are generated with the bounded rationality of decision makers. In this paper, we propose a general online learning framework to deal with this learning problem using inverse multiobjective optimization. More precisely, we develop two online learning algorithms with implicit update rules which can handle noisy data. Numerical results show that both algorithms can learn the parameters with great accuracy and are robust to noise.
