论文标题
学习通用的关系启发式网络，用于模型不足计划
Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning
论文作者
论文摘要
计算目标指导行为对于设计有效的AI系统至关重要。由于计划的计算复杂性，当前方法主要依赖于手工编码的符号动作模型和手动编码的启发式功能发电机来提高效率。关于此类问题的学习启发式方法是有限的，因为它们很难应用于与培训数据中的物体和对象数量有很大不同的物体和对象数量的问题。本文在没有符号动作模型的情况下使用深层神经网络使用输入谓词词汇，但对对象名称和数量不可知。它使用抽象状态表示来促进数据有效，可推广的学习。对一系列基准域的经验评估表明，与先前的方法相比，通过该方法计算的普遍启发式方法可以轻松地转移到不同对象和对象数量的问题上，并且对象数量比训练数据中的物体大得多。
Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded symbolic action models and hand-coded heuristic-function generators for efficiency. Learned heuristics for such problems have been of limited utility as they are difficult to apply to problems with objects and object quantities that are significantly different from those in the training data. This paper develops a new approach for learning generalized heuristics in the absence of symbolic action models using deep neural networks that utilize an input predicate vocabulary but are agnostic to object names and quantities. It uses an abstract state representation to facilitate data efficient, generalizable learning. Empirical evaluation on a range of benchmark domains show that in contrast to prior approaches, generalized heuristics computed by this method can be transferred easily to problems with different objects and with object quantities much larger than those in the training data.
