🤖 AI Summary
This work addresses the coarse-grained explanation problem of Linear Temporal Logic (LTL) rules in eXplainable AI Planning (XAIP). We propose the Rule-State Assessment (RSA) framework, the first approach to *instantaneously* classify, at each timestep along a single execution trace, the status of every LTL rule as active, satisfied, inactive, or violated. Methodologically, RSA leverages formal LTL semantics and integrates symbolic trace execution with a rule-state classification algorithm, enabling fine-grained, queryable, and semantically precise behavioral attribution. Unlike conventional trace-level summaries, RSA supports real-time, dynamic, and multi-rule-coordinated tracking of behavioral evolution. Experimental evaluation demonstrates RSA’s effectiveness as a post-hoc diagnostic tool, significantly enhancing the transparency and debuggability of agent decision-making processes.
📝 Abstract
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.