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Beyond Static Assumptions: The Predictive Justified Perspective Model for Epistemic Planning

Guang Hu Weijia Li Yangmengfei Xu

Aug 2026 · Proceedings of the International Symposium on Combinatorial Search · 0 citations

TL;DR

The Predictive Justified Perspective (PJP) model is formally defined and proved that it retains the computational efficiency of state-based planning while ensuring logically sound belief reasoning (satisfying the KD45 axioms).

Abstract

Epistemic Planning (EP) creates agents capable of reasoning about the nested beliefs of others. However, existing frameworks fundamentally rely on the ``static-environment'' assumption inherited from classical planning. This constraint limits their applicability in dynamic settings where variables evolve independently of agent actions (e.g., moving targets or falling objects). To relax this assumption, we introduce the Predictive Justified Perspective (PJP) model. Unlike previous models that assume unobserved variables remain the same (unless observed evidence suggests otherwise), PJP allows agents to use their history of observations to predict how variables change over time. We formally define this model and prove that it retains the computational efficiency of state-based planning while ensuring logically sound belief reasoning (satisfying the KD45 axioms). Experimental results on the Grapevine benchmark show that PJP successfully extends EP to dynamic environments, allowing agents to reason about nested beliefs regarding changing values—a capability absent in prior work.

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