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The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems.
Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]
Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system dingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.
A distinguishing feature of Answer Set Programming is that all atoms belonging to a stable model must be founded. That is, an atom must not only be true but provably true. This can be made precise by means of the constructive logic of Here-and-There, whose equilibrium models correspond to stable models. One way of looking at foundedness is to regard Boolean truth values as ordered by letting true be greater than false. Then, each Boolean variable takes the smallest truth value that can be proven for it. This idea was generalized by Aziz to ordered domains and applied to constraint satisfaction problems. As before, the idea is that a, say integer, variable gets only assigned to the smallest integer that can be justified. In this paper, we present a logical reconstruction of Aziz’ idea in the setting of the logic of Here-and-There. More precisely, we start by defining the logic of Here-and-There with lower bound founded variables along with its equilibrium models and elaborate upon its formal properties. Finally, we compare our approach with related ones and sketch future work.
teaspoon
(2018)
Answer Set Programming (ASP) is an approach to declarative problem solving, combining a rich yet simple modeling language with high performance solving capacities. We here develop an ASP-based approach to curriculum-based course timetabling (CB-CTT), one of the most widely studied course timetabling problems. The resulting teaspoon system reads a CB-CTT instance of a standard input format and converts it into a set of ASP facts. In turn, these facts are combined with a first-order encoding for CB-CTT solving, which can subsequently be solved by any off-the-shelf ASP systems. We establish the competitiveness of our approach by empirically contrasting it to the best known bounds obtained so far via dedicated implementations. Furthermore, we extend the teaspoon system to multi-objective course timetabling and consider minimal perturbation problems.
plasp 3
(2019)
We describe the new version of the Planning Domain Definition Language (PDDL)-to-Answer Set Programming (ASP) translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.