论文标题
基于模型的计划中的广义启发式搜索的抽象解释
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
论文作者
论文摘要
基于领域的模型计划者通常通过通过放松或抽象的象征世界模型来构建搜索启发式方法来得出他们的普遍性。我们说明抽象的解释如何作为这些基于抽象的启发式方法的统一框架，将启发式搜索的范围扩展到更丰富的世界模型，这些模型利用更复杂的数据类型和功能（例如集合，几何形状），甚至具有不确定性和概率效应的模型。这些启发式方法也可以与学习相结合，从而使代理商可以通过抽象衍生的信息在新颖的世界模型中开始计划，这些信息随后通过经验来完善。这表明抽象的解释可以在建立通用推理系统中起关键作用。
Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate how abstract interpretation can serve as a unifying framework for these abstraction-based heuristics, extending the reach of heuristic search to richer world models that make use of more complex datatypes and functions (e.g. sets, geometry), and even models with uncertainty and probabilistic effects. These heuristics can also be integrated with learning, allowing agents to jumpstart planning in novel world models via abstraction-derived information that is later refined by experience. This suggests that abstract interpretation can play a key role in building universal reasoning systems.
