Continuous Decision Diagrams for Machine Learning and Decision-theoretic AI Planning

机器学习和决策论人工智能规划中的一个关键挑战是现有方法无法有效和准确地推理分段连续函数。这些功能出现在不同的任务中，例如偏好学习和交通信号的实时优化。例如，在后一个应用领域，最优规划者必须推理当信号改变时发生的交通流的分段连续突发。拟议的研究计划直接攻击这一挑战，通过进一步发展的连续决策图及其应用程序的问题，从偏好学习和启发关键的在线商务，以优化交通信号控制的高度拥挤的城市环境的关键。

A key challenge in both Machine Learning and Decision-theoretic AI Planning is the inability of existing methods to efficiently and accurately reason about piecewise continuous functions. Such functions arise in diverse tasks such as preference learning and real-time optimization of traffic signals. For example, in the latter application area, optimal planners must reason about piecewise continuous bursts of traffic flow that occur when signals change. The proposed research program directly attacks this challenge through the further development of continuous decision diagrams and their application to problems ranging from preference learning and elicitation critical for online commerce to optimized traffic signal control critical for highly congested urban environments.