Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach

Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perf...

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Veröffentlicht in:The Journal of artificial intelligence research Jg. 79; S. 725 - 776
Hauptverfasser: Meli, Daniele, Castellini, Alberto, Farinelli, Alessandro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco AI Access Foundation 01.01.2024
Schlagworte:
Computing time
Declarative programming
Heuristic
Logic programming
Markov processes
Mathematical programming
Neural networks
Semantics
Solvers
Specifications
Uncertainty
ISSN:1076-9757, 1076-9757, 1943-5037
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Zusammenfassung:Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distribution. Approximate solvers based on Monte Carlo sampling show great success to relax the computational demand and perform online planning. However, scaling to complex realistic domains with many actions and long planning horizons is still a major challenge, and a key point to achieve good performance is guiding the action-selection process with domain-dependent policy heuristics which are tailored for the specific application domain.  We propose to learn high-quality heuristics from POMDP traces of executions generated by any solver.  We convert the belief-action pairs to a logical semantics, and exploit data- and time-efficient Inductive Logic Programming (ILP) to generate interpretable belief-based policy specifications, which are then used as online heuristics. We evaluate thoroughly our methodology on two notoriously challenging POMDP problems, involving large action spaces and long planning horizons, namely, rocksample and pocman. Considering different state-of-the-art online POMDP solvers, including POMCP, DESPOT and AdaOPS, we show that learned heuristics expressed in Answer Set Programming (ASP) yield performance superior to neural networks and similar to optimal handcrafted task-specific heuristics within lower computational time. Moreover, they well generalize to more challenging scenarios not experienced in the training phase (e.g., increasing rocks and grid size in rocksample, incrementing the size of the map and the aggressivity of ghosts in pocman).
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ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.1.15826