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.
翻译:以一般模式为基础的一般模型规划者往往通过通过放宽或抽象的象征性世界模型来构建搜索理论来得出其普遍性。我们举例说明抽象解释如何能成为这些基于抽象的基于理论的研究的统一框架,将休养研究的范围扩大到利用更复杂的数据类型和功能(例如数据集、几何学)的更富裕的世界模型,甚至具有不确定性和概率效应的模型。这些休养学也可以与学习结合起来,使代理商能够通过抽象衍生的信息启动新的世界模型的规划,然后通过经验加以完善。这表明抽象解释可以在建立普遍推理系统方面发挥关键作用。