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A Rational Extension of Stable Model Semantics to the Full Propositional Language

机译:将稳定的模型语义学合理地扩展到全命题语言

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Answer set programming is the most appreciated framework for non-monotonic reasoning.Stable model semantics,as the semantics behind this success,has been subject to many extensions.The two main such extensions are equilibrium models and FLP semantics.Despite their very interesting foundations,they both have two problems: they cannot guarantee either minimality,or rationality of their intended models.That is,both these semantics allow models in which some atoms are self-justified (i.e.,the only possible reason for including those atoms in the model are those atoms themselves).Present paper extends stable model semantics to the full propositional language while guaranteeing both properties above.Our extension is called supported because it guarantees the existence of noncircular justifications for all atoms in a supported model.These goals are achieved through a form of completion in intuitionistic logic.We also discuss how supported models relate to other semantics for non-monotonic reasoning such as equilibrium models.Finally,we discuss the complexity of reasoning about supported models and show that the complexity of brave/cautious reasoning in supported semantics remains as before,i.e.,the rationality property comes for no additional cost.
机译:答案集编程是非单调推理最受赞赏的框架。稳定模型语义作为此成功背后的语义,经历了许多扩展。平衡模型和FLP语义是这两个主要扩展,尽管它们非常有趣的基础,它们都有两个问题:它们不能保证其预期模型的最小性或合理性。也就是说,这两种语义都允许模型中的某些原子是自称的(即,将这些原子包括在模型中的唯一可能原因是这些原子本身)。目前的论文将稳定的模型语义扩展到完整的命题语言,同时保证了上述两个属性。我们的扩展被称为支持,因为它保证了所支持的模型中所有原子的非圆形合理性的存在。这些目标是通过某种形式实现的关于直觉逻辑的完成。我们还讨论了非单调区域的支持模型如何与其他语义相关最后,我们讨论了关于支持模型的推理的复杂性,并证明了在支持语义中勇敢/谨慎推理的复杂性仍然保持不变,即,合理性属性不会带来额外的成本。

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