Extending Answer Set Programming

一个计算问题可以通过设计和实现一个算法来解决，或者通过用一种直观的建模语言来表达它，该语言附带了用于评估用该语言编写的表达式的软件。后者是所谓的陈述性问题解决的一个例子。本研究的目标是扩展回答集编程（ASP），这已成为一个有前途的声明式范式解决计算问题，通过解决两个重要问题：（a）集成开放世界与封闭世界的推理，（B）使用ASP作为一个统一的语言建模和推理领域以及可废止的本体知识。* ASP的主要思想是，给定的问题是以约束的形式来陈述的，这些约束是在稳定模型（也称为答案集）语义下以基于规则的语言表达的。直观地说，答案集对应于所解决问题的解决方案。当计算出答案集时，用户可以从中提取相应的解决方案。ASP的公式化是在知识表示、逻辑编程和约束满足方面多年研究的结晶。我们的目标是提供一个声明性的语言建模领域的知识和计算工具，处理程序写在这样的语言。具有高度竞争力的ASP求解器已经建成，几个应用程序已经开发，例如，在分子生物学，决策支持系统，规划和调度，解决难题和游戏，以及最近，在推理与Web本体知识起着至关重要的作用。* 在这项研究中，我们提出了两个方向的扩展ASP，每个有显着的特点和优点。一个是紧密集成的ASP与类逻辑的可判定片段，在那里我们将解决结合开放和封闭世界推理的问题。这类推理任务通常出现在复杂异构系统的推理环境中。这种方法的一个优点是它允许采用有效的推理引擎，例如，这些是为本体论推理而开发的。另一个方向涉及ASP与存在规则表示和推理领域和可废止的本体论知识在一个统一的语言。这将导致不同类型的推理的无缝集成，并为构建下一代ASP求解器奠定基础。* 可以预见，许多进一步的应用程序将需要处理领域知识与本体知识，如规划和调度，临床试验和复杂的管理系统，并与Web推理。这项研究的成功将提供所需的见解和计算机制，以促进ASP的采用，解决下一波新的应用程序。

A computational problem can be solved by designing and implementing an algorithm for it, or by expressing it in an intuitive modelling language that comes with software for evaluating expressions written in the language. The latter is an example of what is called declarative problem solving. The goal of this research is to extend Answer Set Programming (ASP for short), which has emerged as a promising declarative paradigm for solving computational problems, by addressing two important problems: (a) on integrating open-world with closed-world reasoning, and (b) on using ASP as a uniform language for modeling and reasoning with domain as well as defeasible ontological knowledge. ****The main idea of ASP is that a given problem is stated in terms of constraints, which are expressed in a rule-based language under the stable model (also called answer set) semantics. Intuitively, an answer set corresponds to a solution to the problem being solved. When an answer set is computed, the user can extract from it the corresponding solution. The formulation of ASP has been crystallized from years of research in knowledge representation, logic programming, and constraint satisfaction. The goal is to provide a declarative language for modelling domain knowledge and computational tools for processing programs written in such a language. With highly competitive ASP solvers already built, several applications have been developed, for example, in molecular biology, decision support systems, planning and scheduling, solving puzzles and games, and more recently, in reasoning with the Web where ontological knowledge plays a critical role. ****In this research, we propose to study two directions of extending ASP, each with distinguished features and merits. One is on a tight integration of ASP with decidable fragments of class logic, where we will address the problem of combining open and closed world reasoning. These kind of reasoning tasks often arise in the context of reasoning with complex heterogeneous systems. One advantage of this approach is that it allows the adoption of efficient inference engines, e.g., those developed for reasoning with ontology. The other direction concerns ASP with existential rules for representing and reasoning with domain and defeasible ontological knowledge in a uniform language. This will result in a seamless integration of different kinds of reasoning and establish foundations for building the next generation ASP solvers. ***It is well anticipated that many further applications would require processing domain knowledge in conjunction with ontological knowledge, such as planning and scheduling, clinical trial and complex management systems, and reasoning with the Web. The success of this research will provide needed insights, and computational mechanisms, for advancing the ASP adoption in solving the next wave of new applications.******