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arXiv · 2505.03668

Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time

Celeste Veronese·Daniele Meli·Alessandro Farinelli
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Abstract

This paper proposes an integration of temporal logical reasoning and Partially Observable Markov Decision Processes (POMDPs) to achieve interpretable decision-making under uncertainty with macro-actions. Our method leverages a fragment of Linear Temporal Logic (LTL) based on Event Calculus (EC) to generate \emph{persistent} (i.e., constant) macro-actions, which guide Monte Carlo Tree Search (MCTS)-based POMDP solvers over a time horizon, significantly reducing inference time while ensuring robust performance. Such macro-actions are learnt via Inductive Logic Programming (ILP) from a few traces of execution (belief-action pairs), thus eliminating the need for manually designed heuristics and requiring only the specification of the POMDP transition model. In the Pocman and Rocksample benchmark scenarios, our learned macro-actions demonstrate increased expressiveness and generality when compared to time-independent heuristics, indeed offering substantial computational efficiency improvements.

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Celeste Veronese, Daniele Meli, Alessandro Farinelli. 2025-05-06. Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time. https://arxiv.org/abs/2505.03668

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