The new field of Explainable Planning (XAIP) has produced a variety of approaches to explain and describe the behavior of autonomous agents to human observers. Many summarize agent behavior in terms of the constraints, or ''rules,'' which the agent adheres to during its trajectories. In this work, we narrow the focus from summary to specific moments in individual trajectories, offering a ''pointwise-in-time'' view. Our novel framework, which we define on Linear Temporal Logic (LTL) rules, assigns an intuitive status to any rule in order to describe the trajectory progress at individual time steps; here, a rule is classified as active, satisfied, inactive, or violated. Given a trajectory, a user may query for status of specific LTL rules at individual trajectory time steps. In this paper, we present this novel framework, named Rule Status Assessment (RSA), and provide an example of its implementation. We find that pointwise-in-time status assessment is useful as a post-hoc diagnostic, enabling a user to systematically track the agent's behavior with respect to a set of rules.

翻译：可解释规划（XAIP）新领域已产生了多种方法，用于向人类观察者解释和描述自主智能体的行为。许多方法通过智能体在其轨迹中遵循的约束或"规则"来总结其行为。在本研究中，我们将视角从概括性总结转向单个轨迹中的特定时刻，提出"逐点时间"视角。我们基于线性时序逻辑（LTL）规则定义的新框架，为每条规则赋予直观状态以描述单个时间步上的轨迹进展；在此，规则被分类为激活态、满足态、非激活态或违例态。给定一条轨迹，用户可查询特定LTL规则在轨迹各时间步上的状态。本文提出这一名为"规则状态评估"（RSA）的新框架，并给出其实施示例。我们发现，逐点时间状态评估作为事后诊断工具非常有用，能够使用户系统地追踪智能体在规则集约束下的行为。