Search-based AI agents are state of the art in many challenging sequential decision-making domains. However, contemporary approaches lack the ability to explain, summarize, or visualize their plans and decisions, and how they are derived from traversing complex spaces of possible futures, contingencies, and eventualities, spanned by the available actions of the agent. This limits human trust in high-stakes scenarios, as well as effective human-AI collaboration. In this paper, we pr