Answer Set Programming for Dynamic Domains

知识表示和推理在应对第四次工业革命中发挥着关键作用，因为它为解决复杂问题提供了灵活和透明的方法。答案集编程是解决知识密集型搜索和优化问题的主要候选者，广泛应用于生产，存储或劳动力管理。 简单来说，ASP是一种基于arule的形式主义，用于建模和解决此类问题。ASP的吸引力在于它将声明性建模语言与高效的求解引擎相结合。 这使我们能够专注于指定一个问题，而不是编程算法来解决。 虽然ASP在学术界和工业界的传播越来越广泛，但仔细观察就会发现，这主要涉及静态或较小的动态领域。 例如，ASP在时间表和劳动力管理等静态领域具有很强的竞争力，而在大量动态领域，例如机器人内部物流，即控制一队机器人车辆在仓库中漫游以履行客户订单。 事实上，在ASP处理静态域和动态域的开发水平之间仍然有相当大的差距。 这是因为它的建模语言以及它的求解机制到目前为止主要针对静态知识，而动态知识只能通过对静态情况的简化来间接处理。 我们从第一原则开始，发展我们方法的逻辑基础。 我们扩展ASP的基础逻辑的概念，从动态，度量和时态逻辑，建立他们的共同语义结构的有限线性轨迹。 反过来，我们确定适当的语言片段，是适合的建模语言fordynamic域。我们利用语义的原则，设计和实现一种新的解决技术，支持我们的时间语言。 它的基石之一将是懒惰的约束求解，因为它使我们能够实现时间的语言结构在一个有效的方式与现有的ASP系统，如我们的系统CLINGO。 最后，我们开发建模和工程技术，将我们的方法应用于工业规模的场景。 为此，我们将机器人内部物流确定为一个完美的基准，因为这个问题是动态的，高度可扩展的，并且结合了大量不同的方面。 这种丰富性不仅使我们能够开发一种多功能的方法来机器人内部物流，而且还将ASP扩展到动态领域的通用技术。

Knowledge representation and reasoning plays a key role in dealing with the fourth industrial revolution since it offers flexible and transparent ways for addressing complex problems. A prime candidate for solving knowledge-intense search and optimization problems, being widespread in production, storage, or workforce management, is Answer Set Programming. ASP, for short, is arule-based formalism for modeling and solving such problems. What makes ASP attractive is its combination of a declarative modeling language with highly effective solving engines. This allows us to concentrate on specifying a problem rather than programming the algorithm for solving. Although ASP experiences an increasing dissemination in academia and industry, a closer look reveals that this concerns mostly static or smaller dynamic domains. For example, ASP is highly competitive in static domains such as time tabling and workforce management, whereas it lags behind when it comes to substantial dynamic ones, as for instance, robotic intra-logistics that is about controlling a fleet of robotic vehicles roaming a warehouse to fulfill customer orders. Infact, there is still quite a chasm between ASP's level of development for addressing static and dynamic domains. This is because its modeling language as well as its solving machinery aim so far primarily at static knowledge, while dynamic knowledge is only indirectly dealt with via reductions to the static case.We address this challenge in the following way. We start from first principles by developing the logical foundations of our approach. We extend ASP's base logic with concepts from dynamic, metric and temporal logic by founding them on the common semantic structure of finite linear traces. In turn, we identify appropriate language fragments that are suitable for a modeling language fordynamic domains. We leverage the semantic principles for designing and implementing a novel solving technology supporting our temporal language. One of its cornerstones will be lazy constraint solving, since it allows us to to realize the temporal language constructs in an effective way with existing ASP systems such as our system CLINGO. Finally, we develop modeling and engineering techniques for applying our approach to industrial-scale scenarios. For this, we identified robotic intra-logistics as a perfect benchmark, since the problem is greatly dynamic, highly scalable, and combines an abundance of different aspects. This richness will not only enable us to develop a versatile approach to robotic intra-logistics but moreover extend ASP to a general purpose technology for dynamic domains.