Beyond Static Assumptions: the Predictive Justified Perspective Model for Epistemic Planning
Guang Hu, Weijia Li, Yangmengfei Xu
TL;DR
This work addresses the static-environment limitation of existing Epistemic Planning (EP) by extending the Justified Perspective (JP) model to a Predictive Justified Perspective (PJP) framework. PJP introduces processual variables that evolve due to environmental dynamics and defines predictive retrieval functions $pr_{type(v)}$ to forecast variable values from past observations, enabling nested beliefs under changing conditions. The model provides formal semantics (KD45-compatible ternary logic), axiomatic validity, and polynomial-time evaluation, with an implementation that integrates into PDDL+-style encodings via external functions $@epi$ and $@jp$. Experimental results in dynamic Grapevine domains demonstrate that PJP can outperform static JP in scenarios with evolving variables, highlighting practical implications for robotics and multi-agent systems where the environment changes independently of agent actions.
Abstract
Epistemic Planning (EP) is an important research area dedicated to reasoning about the knowledge and beliefs of agents in multi-agent cooperative or adversarial settings. The Justified Perspective (JP) model is the state-of-the-art approach to solving EP problems with efficiency and expressiveness. However, all existing EP methods inherit the static environment assumption from classical planning. This limitation hinders the application of EP in fields such as robotics with multi-agent settings, where the environment contains changing variables. In this paper, we propose an extension of the JP model, namely, the Predictive Justified Perspective (PJP) model, to remove this assumption. Instead of assuming that beliefs remain unchanged since the last observation, the PJP model uses all past observations to form predictions about the changing variables. The definition of the prediction function with examples is provided, and it is demonstrated that it can work with arbitrary nesting. We then implemented the PJP model in several well-known domains and compared it with the JP model in the experiments. The results indicated that the PJP model performs exceptionally well across various domains, demonstrating its potential in improving EP applications in robotics.