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.

论文提出了一个基于ConGolog编程语言和非确定情景演算的抽象化代理行为的通用框架，在支持策略推理和策略合成的同时，实现了代理动作和环境反应的分离量化。如果代理在抽象层次有一个(plan/strategy)可以实现目标/完成任务，并且可以在具体层次始终执行非确定性的抽象行为从而实现目标/任务，则存在一个对具体目标/任务的细化的(plan/strategy)实现。

非确定性Situation Calculus动作理论的抽象——扩展版