Answer set programming (ASP) is a popular declarative programming paradigm with a wide range of applications in artificial intelligence. Oftentimes, when modeling an AI problem with ASP, and in particular when we are interested beyond simple search for optimal solutions, an actual solution, differences between solutions, or number of solutions of the ASP program matter. For example, when a user aims to identify a specific answer set according to her needs, or requires the total number of diverging solutions to comprehend probabilistic applications such as reasoning in medical domains. Then, there are only certain problem specific and handcrafted encoding techniques available to navigate the solution space of ASP programs, which is oftentimes not enough. In this paper, we propose a formal and general framework for interactive navigation towards desired subsets of answer sets analogous to faceted browsing. Our approach enables the user to explore the solution space by consciously zooming in or out of sub-spaces of solutions at a certain configurable pace. We illustrate that weighted faceted navigation is computationally hard. Finally, we provide an implementation of our approach that demonstrates the feasibility of our framework for incomprehensible solution spaces.
翻译:设定答案的编程( ASP) 是一种广受欢迎的宣示性编程模式,在人工智能中应用了各种各样的应用。 当模拟与 ASP 的AI 问题时, 特别是当我们感兴趣的不仅仅是简单寻找最佳解决方案、 实际解决方案、 解决方案之间的差别, 或者 ASP 程序需要的解决方案的数量等。 例如, 当用户试图根据她的需求确定一个具体的答案, 或者要求理解概率性应用( 如医学领域的推理) 的多种不同解决方案的总数时。 然后, 通常只有某些特定的问题和手工制作的编码技术, 来浏览 ASP 程序解决方案的空间, 而这往往不够。 在本文中, 我们提出了一个正式和一般的互动式导航框架, 用于对匹配的答案组进行互动浏览。 我们的方法使用户能够通过有意识地在解决方案的子空间内或外按某种可配置的速度来探索解决方案的空间。 我们用计算来说明加权面导航是困难的。 最后, 我们提供了一种方法的实施方法, 以证明我们框架对于无法理解的解决方案空间的可行性。