We introduce a general, flexible, and extensible framework for quantitative and qualitative preferences among the stable models of logic programs. Since it is straightforward to capture propositional theories and constraint satisfaction problems with logic programs, our approach is also relevant to optimization in satisfiability testing and constraint processing. We show how complex preference relations can be specified through user-defined preference types and their arguments. We describe how preference specifications are handled internally by so-called preference programs, which are used for dominance testing. We also provide algorithms for computing one, or all, preferred stable models of a logic program, and study the complexity of these problems. We implemented our approach in the asprin system by means of multi-shot answer set solving technology. We demonstrate the generality and flexibility of our methodology by showing how easily existing preference languages can be implemented in asprin. Finally, we empirically evaluate our contributions and contrast them with dedicated implementations.

我们为逻辑程序的稳定模型中的定量和定性偏好引入了一个通用的、灵活的和可扩展的框架。由于用逻辑程序捕获命题理论和约束满足问题很简单，因此我们的方法也与可满足性测试和约束处理中的优化相关。我们展示了如何通过用户定义的偏好类型及其参数来指定复杂的偏好关系。我们描述了所谓的偏好程序如何在内部处理偏好规范，这些程序用于优势测试。我们还提供用于计算逻辑程序的一个或全部首选稳定模型的算法，并研究这些问题的复杂性。我们在阿司匹林中实施了我们的方法系统采用多镜头答案集求解技术。我们通过展示在asprin中实现现有偏好语言是多么容易来证明我们方法的通用性和灵活性。最后，我们根据经验评估我们的贡献并将其与专用实现进行对比。