Neurosymbolic Action Model Learning under Partial Observability
This paper proposes NeSyAM, a novel neurosymbolic modeling paradigm for action model learning under partial observability, and presents a unified variational framework for theoretically analysing the limitations of existing methods compared to this proposed approach.
Adem Kikaj, Lennert De Smet, Giuseppe Marra et al.
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