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Foundations of a DPLL-Based Solver for Fuzzy Answer Set Programs

机译：基于DPLL的模糊答案集程序求解器的基础

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Recent years have witnessed the effort to extend answer set programming (ASP) with properties of fuzzy logic. The result of this combination is fuzzy answer set programming (FASP), a powerful framework for knowledge representation and non-monotonic reasoning with graded levels of truth. The various results in solving FASP make use of transformations into fuzzy satisfiability (SAT) problems, optimization programs, satisfiability modulo theories (SMT), or classical ASP, each of which comes with limitations or scaling problems. Moreover, most of the research revolves around Godel and Lukasiewicz semantics. The former approach is elegant in its attempt to generalize well-known methods in classical ASP to the fuzzy case. In our work we seek to extend this approach under the product semantics, utilizing the fuzzy generalization of the DPLL algorithm. As such, we design the inner works of a DPLL-based fuzzy SAT solver for propositional product logic, which should provide foundations for the technical implementation of the solver.
机译：近年来，目睹了扩展具有模糊逻辑属性的答案集编程（ASP）的工作。这种结合的结果是模糊答案集编程（FASP），这是一个具有分级真相的知识表示和非单调推理的强大框架。解决FASP的各种结果都利用了转换为模糊可满足性（SAT）问题，优化程序，可满足性模理论（SMT）或经典ASP的方法，每种方法都有局限性或缩放性问题。此外，大多数研究围绕Godel和Lukasiewicz语义展开。前一种方法很好地尝试将经典ASP中的著名方法推广到模糊情况。在我们的工作中，我们试图利用DPLL算法的模糊概括在产品语义下扩展这种方法。因此，我们针对命题乘积逻辑设计了基于DPLL的模糊SAT求解器的内部工作，这应该为求解器的技术实现提供基础。

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