  Monte Carlo tree search (MCTS) is one of the most capable online search
algorithms for sequential planning tasks, with significant applications in
areas such as resource allocation and transit planning. Despite its strong
performance in real-world deployment, the inherent complexity of MCTS makes it
challenging to understand for users without technical background. This paper
considers the use of MCTS in transportation routing services, where the
algorithm is integrated to develop optimized route plans. These plans are
required to meet a range of constraints and requirements simultaneously,
further complicating the task of explaining the algorithm's operation in
real-world contexts. To address this critical research gap, we introduce a
novel computation tree logic-based explainer for MCTS. Our framework begins by
taking user-defined requirements and translating them into rigorous logic
specifications through the use of language templates. Then, our explainer
incorporates a logic verification and quantitative evaluation module that
validates the states and actions traversed by the MCTS algorithm. The outcomes
of this analysis are then rendered into human-readable descriptive text using a
second set of language templates. The user satisfaction of our approach was
assessed through a survey with 82 participants. The results indicated that our
explanatory approach significantly outperforms other baselines in user
preference.
