The DLV System for Knowledge Representation and Reasoning

This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to $\Delta^P_3$-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of DLV, and by deriving new complexity results we chart a complete picture of the complexity of this language and important fragments thereof. Furthermore, we illustrate the general architecture of the DLV system which has been influenced by these results. As for applications, we overview application front-ends which have been developed on top of DLV to solve specific knowledge representation tasks, and we briefly describe the main international projects investigating the potential of the system for industrial exploitation. Finally, we report about thorough experimentation and benchmarking, which has been carried out to assess the efficiency of the system. The experimental results confirm the solidity of DLV and highlight its potential for emerging application areas like knowledge management and information integration.Comment: 56 pages, 9 figures, 6 table

Deep Learning for the Generation of Heuristics in Answer Set Programming: A Case Study of Graph Coloring

Answer Set Programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domain-specific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver wasp can be improved

Reusable Knowledge-based Components for Building Software Applications: A Knowledge Modelling Approach

In computer science, different types of reusable components for building software applications were proposed as a direct consequence of the emergence of new software programming paradigms. The success of these components for building applications depends on factors such as the flexibility in their combination or the facility for their selection in centralised or distributed environments such as internet. In this article, we propose a general type of reusable component, called primitive of representation, inspired by a knowledge-based approach that can promote reusability. The proposal can be understood as a generalisation of existing partial solutions that is applicable to both software and knowledge engineering for the development of hybrid applications that integrate conventional and knowledge based techniques. The article presents the structure and use of the component and describes our recent experience in the development of real-world applications based on this approach

The Answer Set Programming (ASP) Competition

is a biannual event for evaluating declarative knowledge representation systems on hard and demanding AI problems. The competition consists of two main tracks: the ASP System Track and the Model & Solve Track. The traditional System Track compares dedicated answer set solvers on ASP benchmarks, while the Model & Solve Track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP Competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses about the lessons learned so far. 1 A Brief History Answer Set Programming (ASP) is a well-established paradigm of declarative programming with roots in the stable models semantics for logic programs (Gelfond and Lifschitz, 1991; Niemelä, 1999; Marek and Truszczyński, 1999). The main goal of ASP is to provide a versatile declarative modeling framework with many attractive characteristics. These features allow to turn—with little to no effort—problem statements of computationally hard problems into executable formal specifications, also called Answer Set Programs. These programs can be used to describe and reason over problems in a large variety of domains, such as commonsense and agent reasoning, diagnosis, deductive databases, planning, bioinformatics, scheduling and timetabling. See (Brewka et al., 2012) for an overview, while for introductory material on ASP, the reader might refer to (Baral, 2003; Eiter et al., 2009). ASP has a close relationship to other declarative modeling paradigms and languages, such as SA

Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there is constraint logic programming which computes a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are systems based on stable model semantics, abductive systems, and first order logic model generators which compute solutions as models of some theory. This paper compares these different approaches from the point of view of knowledge representation (how declarative are the programs) and from the point of view of performance (how good are they at solving typical problems).Comment: 15 pages, 3 eps-figure