Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time
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| Format: | Preprint |
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2025
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| _version_ | 1866918011555807232 |
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| author | Veronese, Celeste Meli, Daniele Farinelli, Alessandro |
| author_facet | Veronese, Celeste Meli, Daniele Farinelli, Alessandro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_03668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time Veronese, Celeste Meli, Daniele Farinelli, Alessandro Artificial Intelligence 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. |
| title | Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.03668 |