Inner-Learning: From Hand-Coded Beliefs to Declarative Knowledge in Trick-Taking Games
arXiv:2610.38304v1 Announce Type: new Abstract: We present a declarative approach to belief-state modeling in trick-taking card games. Starting from a state-of-the-art Skat player, we automatically extract its hand-coded inference rules into a domain-specific language built on the primitive $\neg\mathsf{holds}(P,C)$, denoting that player $P$ cannot hold any card in set $C$. Round-trip code generation verifies that the extracted rules reproduce the original engine's intended behavior, up to thre…