判断综合定性约束网络的可满足性
Deciding the Satisfiability of Combined Qualitative Constraint Networks
AI总结：
本文提出统一多种定性形式化方法的框架，通过两个定理证明可满足性判断的多项式复杂性，并扩展了定性形式化方法的定义。
摘要：
在人工智能研究中，各种形式的推理中，定性推理使在不精确、不完整信息的上下文中能够推断新知识而无需数值。在本文中，我们提出了一种正式框架，统一了多种定性形式化方法的扩展和组合形式，包括多尺度推理、时间序列和松散整合。该框架使能够在这些组合和扩展的上下文中进行推理，同时以统一的方式研究可满足性判断及其复杂性。特别是，我们建立了两个互补定理，保证可满足性判断是多项式的，并利用它们恢复大小-拓扑组合的已知结果。我们还扩展了定性形式化方法的主要定义，以包括文献中未包含的定性形式化方法，这对于组合的上下文至关重要。
英文摘要：
Among the various forms of reasoning studied in the context of artificial intelligence, qualitative reasoning makes it possible to infer new knowledge in the context of imprecise, incomplete information without numerical values. In this paper, we propose a formal framework unifying several forms of extensions and combinations of qualitative formalisms, including multi-scale reasoning, temporal sequences, and loose integrations. This framework makes it possible to reason in the context of each of these combinations and extensions, but also to study in a unified way the satisfiability decision and its complexity. In particular, we establish two complementary theorems guaranteeing that the satisfiability decision is polynomial, and we use them to recover the known results of the size-topology combination. We also generalize the main definition of qualitative formalism to include qualitative formalisms excluded from the definitions of the literature, important in the context of combinations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
