Abstract We analyze the computational complexity of probabilistic logic programming with constraints, disjunctive heads, and aggregates such as sum and max. We consider propositional programs and relational programs with bounded-arity predicates, and look at cautious reasoning (i.e., computing the smallest probability of an atom over all probability models), cautious explanation (i.e., finding an interpretation that maximizes the lower probability of evidence) and cautious maximum-a-posteriori (i.e., finding a partial interpretation for a set of atoms that maximizes their lower probability conditional on evidence) under Lukasiewicz's credal semantics.

中文翻译：

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概率答案集编程的复杂性结果

摘要 我们分析了具有约束、析取头和聚合（如 sum 和 max）的概率逻辑编程的计算复杂性。我们考虑具有有限元谓词的命题程序和关系程序，并着眼于谨慎推理（即在所有概率模型中计算原子的最小概率）、谨慎解释（即找到最大化证据的较低概率的解释）在 Lukasiewicz 的可信语义下，谨慎的最大后验（即，找到一组原子的部分解释，以在证据为条件下最大化它们的较低概率）。