Weighted knowledge bases for description logics with typicality under a "concept-wise'' multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a $\Pi^p_2$ upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfils the lack by providing a $P^{NP[log]}$-completeness result and new ASP encodings that deal with weighted knowledge bases with large search spaces.
翻译：在“概念性”的多优先语义学下,用于描述典型逻辑描述的加权知识基础对多视谱谱提供了逻辑解释。在这方面,“答案设置程序”(ASP)已证明适合于处理有限、价值众多的案例中的不可行的推理,为问题的复杂性提供了上限为$\Pi<unk> p_2美元,但对于确切的复杂性并不十分清楚,只提供了概念执行的证明。本文通过提供$P<unk> NP[log]$-nationalnational-resulture 和涉及大型搜索空间加权知识基础的新的ASP编码来弥补缺陷。</s>
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