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
Online-Zugang:	Volltext
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