Effective Explanations Support Planning Under Uncertainty

2026-05-08

AI Summary

This study addresses the challenge of generating natural language explanations that effectively guide human navigation and decision-making in uncertain environments. We propose the first utility-driven procedural explanation framework, which translates natural language explanations into policy priors and value maps for planning agents operating in partially observable settings. Explanation quality is quantified through path efficiency and replanning frequency. Integrating large language models, POMDP-based planning algorithms, and a preregistered behavioral experiment, we collected 1,200 explanation utterances across 24 maps. Results demonstrate that high-quality explanations significantly improve human navigation performance compared to both low-quality explanations and a no-explanation baseline, establishing—for the first time—a direct link between executable planning performance and explanation quality.

Abstract

Explaining how to get from A to B can be challenging. It requires mentally simulating what the listener will do based on what they are told. To capture this process, we propose a computational model that converts utterances into action plans: a large language model translates an explanation into program-like guidance (a policy prior and value map), and a planning agent executes it under partial observability. We score explanations by the efficiency and reliability of the resulting paths, penalizing replanning. Across four preregistered experiments, we collect a corpus of 1,200 explanations over 24 maps, elicit helpfulness judgments, measure baseline navigation, and test behavior with explanations of differing quality. Higher-scored explanations are judged more helpful and improve navigation: participants with explanations outperform those without, and high-scoring explanations help more than low-scoring ones. Together, these results show procedural explanation as utility-guided communication shaped by how language can be grounded into action under uncertainty.

Problem

• explanation
• planning under uncertainty
• navigation
• procedural communication
• partial observability

Innovation

• procedural explanation
• utility-guided communication
• language-to-action grounding
• planning under uncertainty
• policy prior