We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. We assume that we have both an abstract and a concrete nondeterministic basic action theory, and a refinement mapping which specifies how abstract actions, decomposed into agent actions and environment reactions, are implemented by concrete ConGolog programs. This new setting supports strategic reasoning and strategy synthesis, by allowing us to quantify separately on agent actions and environment reactions. We show that if the agent has a (strong FOND) plan/strategy to achieve a goal/complete a task at the abstract level, and it can always execute the nondeterministic abstract actions to completion at the concrete level, then there exists a refinement of it that is a (strong FOND) plan/strategy to achieve the refinement of the goal/task at the concrete level.

翻译：我们基于非确定情境演算与ConGolog编程语言，构建了一个通用框架，用于抽象运行于非确定域中的智能体行为，即智能体无法控制非确定动作结果的情形。我们假设同时拥有一套抽象层与具体层的非确定基本行为理论，以及一个精化映射，该映射将抽象动作（分解为智能体动作与环境反应）转化为具体的ConGolog程序实现。这一新框架通过分别量化智能体动作与环境反应，支持策略推理与策略综合。我们证明：若智能体在抽象层上拥有达成目标/完成任务（强非确定性完全可观测）的计划/策略，且能在具体层上始终完整执行非确定抽象动作，则存在该计划的精化版本，可在具体层上实现目标/任务的精化版计划/策略。