Responsibility anticipation is the process of determining if the actions of an individual agent may cause it to be responsible for a particular outcome. This can be used in a multi-agent planning setting to allow agents to anticipate responsibility in the plans they consider. The planning setting in this paper includes partial information regarding the initial state and considers formulas in linear temporal logic as positive or negative outcomes to be attained or avoided. We firstly define attribution for notions of active, passive and contributive responsibility, and consider their agentive variants. We then use these to define the notion of responsibility anticipation. We prove that our notions of anticipated responsibility can be used to coordinate agents in a planning setting and give complexity results for our model, discussing equivalence with classical planning. We also present an outline for solving some of our attribution and anticipation problems using PDDL solvers.

翻译：责任预期是指判断个体智能体的行为是否可能导致其对特定结果负责的过程。在多智能体规划场景中，该过程可使智能体在其考虑的规划方案中预判自身责任。本文的规划设定包含关于初始状态的部分信息，并将线性时序逻辑中的公式作为需达成或避免的正向/负向结果。我们首先定义了主动性、被动性和贡献性责任归因概念，并探讨其智能体变体，进而据此构建责任预期的形式化定义。我们证明了所提出的责任预期概念可用于协调规划场景中的智能体，给出了模型的计算复杂度分析结果，并讨论了与经典规划的等价性。最后，我们概述了利用PDDL求解器解决部分归因与预期问题的可行方案。