Abstract Product configuration is a successful application of answer set programming (ASP). However, challenges are still open for interactive systems to effectively guide users through the configuration process. The aim of our work is to provide an ASP-based solver for interactive configuration that can deal with large-scale industrial configuration problems and that supports intuitive user interfaces (UIs) via an application programming interface (API). In this paper, we focus on improving the performance of automatically completing a partial configuration. Our main contribution enhances the classical incremental approach for multi-shot solving by four different smart expansion functions. The core idea is to determine and add specific objects or associations to the partial configuration by exploiting cautious and brave consequences before checking for the existence of a complete configuration with the current objects in each iteration. This approach limits the number of costly unsatisfiability checks and reduces the search space, thereby improving solving performance. In addition, we present a UI that uses our API and is implemented in ASP.

Abstract We present plingo, an extension of the answer set programming (ASP) system clingo that incorporates various probabilistic reasoning modes. Plingo is based on $\textit{Lpmln}^{\pm }$ , a simple variant of the probabilistic language Lpmln, which follows a weighted scheme derived from Markov logic. This choice is motivated by the fact that the main probabilistic reasoning modes can be mapped onto enumeration and optimization problems and that $\textit{Lpmln}^{\pm }$ may serve as a middle-ground formalism connecting to other probabilistic approaches. Plingo offers three alternative frontends, for Lpmln, P-log, and ProbLog. These input languages and reasoning modes are implemented by means of clingo’s multi-shot and theory-solving capabilities. In this way, the core of plingo is an implementation of $\textit{Lpmln}^{\pm }$ in terms of modern ASP technology. On top of that, plingo implements a new approximation technique based on a recent method for answer set enumeration in the order of optimality. Additionally, in this work, we introduce a novel translation from $\textit{Lpmln}^{\pm }$ to ProbLog. This leads to a new solving method in plingo where the input program is translated and a ProbLog solver is executed. Our empirical evaluation shows that the different solving approaches of plingo are complementary and that plingo performs similarly to other probabilistic reasoning systems.