Sketched Answer Set Programming

Answer Set Programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models is not trivial. We propose a novel method, called Sketched Answer Set Programming (SkASP), aiming at supporting the user in resolving this issue. The user writes an ASP program while marking uncertain parts open with question marks. In addition, the user provides a number of positive and negative examples of the desired program behaviour. The sketched model is rewritten into another ASP program, which is solved by traditional methods. As a result, the user obtains a functional and reusable ASP program modelling her problem. We evaluate our approach on 21 well known puzzles and combinatorial problems inspired by Karp's 21 NP-complete problems and demonstrate a use-case for a database application based on ASP.Comment: 15 pages, 11 figures; to appear in ICTAI 201

Multi-Core Unit Propagation in Functional Languages

Answer Set Programming is a declarative modeling paradigm enabling specialists in diverse disciplines to describe and solve complicated problems. Growth in high performance computing is driving ever smarter and more scalable parallel answer set solvers. To improve on today\u27s cutting-edge, researchers need to develop increasingly intelligent methods for analysis of a solver\u27s runtime information. Reflecting on the solver\u27s search state typically pauses its progress until the analysis is complete. This work introduces methods from the domain of parallel functional programming and immutable type theory to construct a representation of the search state that is both amenable to introspection and efficiently scalable across multiple processor cores

System aspmt2smt: Computing ASPMT Theories by SMT Solvers

Abstract. Answer Set Programming Modulo Theories (ASPMT) is an approach to combining answer set programming and satisfiability modulo theories based on the functional stable model semantics. It is shown that the tight fragment of ASPMT programs can be turned into SMT instances, thereby allowing SMT solvers to compute stable models of ASPMT programs. In this paper we present a compiler called ASPSMT2SMT, which implements this translation. The system uses ASP grounder GRINGO and SMT solver Z3. GRINGO partially grounds input programs while leaving some variables to be processed by Z3. We demonstrate that the system can effectively handle real number computations for reasoning about continuous changes.

Generating Optimal Code Using Answer Set Programming

This paper presents the Total Optimisation using Answer Set Technology (TOAST) system, which can be used to generate optimal code sequences for machine architectures via a technique known as superoptimisation. Answer set programming (ASP) is utilised as the modelling and computational framework for searching over the large, complex search spaces and for proving the functional equivalence of two code sequences. Experimental results are given showing the progress made in solver performance over the previous few years, along with an outline of future developments to the system and applications within compiler toolchains