A Sequent Calculus for Answer Set Entailment

In the twenty years since the creation of the stable model semantics[4] for logic programs and inclusion of both classical and negation as failure, answer set programming (ASP)[1] has grown from a small fledgeling field within logic programming to a maturing field of its own. In this extended abstract we discuss some future application areas for ASP and theoretic and implementational problems that need to be addressed in order to make them feasible. With the increase in efficiency of the answer set solvers and a better understanding of the formalism, its advantages and disadvantages, more applications areas for ASP are found. While traditional application domains were mainly in the knowledge representation and reasoning area, we now also see more and more applications in areas were competitors like SAT or CSP were considered better alternatives. We believe, these current applications are just the tip of the iceberg. In years to come, we will see more and more successful applications plus further expansion and improvements on existing applications. In recent years we have seen a lot of ASP activity in the fields of the semantic web and web-services with specific workshops dedicated to the topic1. We believe that these fields hold more potential for years to come. For example, ASP could also be used for match-making and service coordination and orchestration. Another application area of ASP is multi-agent systems. In most cases this was always on the more theoretical side of applications. To our knowledge, nobody has used ASP to construct a running multi-agent system where the agents use ASP for reasoning purposes. With our current solvers, we should be able to provide such a system. ASP has also be applied in wide variety of other domains like planning, diagnosis, language-evolution, policy-design, bio-informatics, compiler optimisation, music composition, security, cryptography and game design. Most of these application are in a prototype phase, demonstrating that ASP could be of value in this domain and are slowly making their way to conferences and journals in the domain they are addressing. By doing some domain experts will get to know ASP and its capabilities. In time this will lead to them pointing ASP researchers to similar problems in the same domain. So in the medium term, we believe we can look forward to more ASP applications in bio-informatics, e-health, medicine, e-government and engineering. Normally we refer to ASP as a problem solving paradigm. We model the problem as an ASP program such that the answer sets correspond to the solutions of this problem.

Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs and theories. When intertheory relations are studied in ASP, or in the more general form of equilibrium logic, they are usually understood in the form of comparisons of the answer sets or equilibrium models of theories or programs. This is the case for strong and uniform equivalence and their relativised and projective versions. However, there are many potential areas of application of ASP for which query answering is relevant and a comparison of programs in terms of what can be inferred from them may be important. We formulate and study some natural equivalence and entailment concepts for programs and theories that are couched in terms of inference and query answering. We show that, for the most part, these new intertheory relations coincide with their model-theoretic counterparts. We also extend some previous results on projective entailment for theories and for the new connective called fork.

Equilibrium logic, introduced by David Pearce, extends the concept of an answer set from logic programs to arbitrary sets of formulas. Logic programs correspond to the special case in which every formula is a “rule” — an implication that has no implications in the antecedent (body) and consequent (head). The semantics of equilibrium logic looks very different from the usual definitions of an answer set in logic programming, as it is based on Kripke models. In this paper we propose a new definition of equilibrium logic which uses the concept of a reduct, as in the standard definition of an answer set. Second, we apply the generalized concept of an answer set to the problem of defining the semantics of aggregates in answer set programming. We propose, in particular, a semantics for weight constraints that covers the problematic case of negative weights. Our semantics of aggregates is an extension of the approach due to Faber, Leone, and Pfeifer to a language with choice rules and, more generally, arbitrary rules with nested expressions.

Answer Set Programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer sets. However, a variety of probabilistic applications in reasoning or logic programming require counting answer sets. While counting can be done by enumeration, simple enumeration becomes immediately infeasible if the number of solutions is high. On the other hand, approaches to exact counting are of high worst-case complexity. In fact, in propositional model counting, exact counting becomes impractical. In this work, we present a scalable approach to approximate counting for answer set programming. Our approach is based on systematically adding XOR constraints to ASP programs, which divide the search space. We prove that adding random XOR constraints partitions the answer sets of an ASP program. In practice, we use a Gaussian elimination-based approach by lifting ideas from SAT to ASP and integrating it into a state of the art ASP solver, which we call ApproxASP. Finally, our experimental evaluation shows the scalability of our approach over the existing ASP systems.

Certain extensions of Nelson's constructive logic N with strong negation have recently become important in arti.cial intelligence and nonmonotonic reasoning, since they yield a logical foundation for answer set programming (ASP). In this paper we look at some extensions of Nelson's .rst-order logic as a basis for de.ning nonmonotonic inference relations that underlie the answer set programming semantics. The extensions we consider are those based on 2-element, here-and-there Kripke frames. In particular, we prove completeness for .rst-order here-and-there logics, and their minimal strong negation extensions, for both constant and varying domains. We choose the constant domain version, which we denote by QNc5, as a basis for de.ning a .rst-order nonmonotonic extension called equilibrium logic. We establish several metatheoretic properties of QNc5, including Skolem forms and Herbrand theorems and Interpolation, and show that the .rst-oder version of equilibrium logic can be used as a foundation for answer set inference.

In recent years, answer set programming (ASP) has been extended to deal with multivalued predicates. The resulting formalismsallow for the modeling of continuous problems as elegantly as ASP allows for the modeling of discrete problems, by combining thestable model semantics underlying ASP with fuzzy logics. However, contrary to the case of classical ASP where manyefficient solvers have been constructed, to date there is no efficient fuzzy ASP solver. A well-knowntechnique for classical ASP consists of translating an ASP programPto a propositional theory whose models exactlycorrespond to the answer sets ofP. In this paper, we show how this idea can be extended to fuzzy ASP, paving the wayto implement efficient fuzzy ASP solvers that can take advantage of existing fuzzy logic reasoners.

Adapting techniques from database theory in order to optimize Answer Set Programming (ASP) systems, and in particular the grounding components of ASP systems, is an important topic in ASP. In recent years, the Magic Set method has received some interest in this setting, and a variant of it, called Dynamic Magic Set, has been proposed for ASP. However, this technique has a caveat, because it is not correct (in the sense of being query-equivalent) for all ASP programs. In a recent work, a large fragment of ASP programs, referred to assuper-coherent programs, has been identified, for which Dynamic Magic Set is correct. The fragment contains all programs which possess at least one answer set, no matter which set of facts is added to them. Two open question remained: How complex is it to determine whether a given program is super-coherent? Does the restriction to super-coherent programs limit the problems that can be solved? Especially the first question turned out to be quite difficult to answer precisely. In this paper, we formally prove that deciding whether a propositional program is super-coherent is Π3P-complete in the disjunctive case, while it is Π2P-complete for normal programs. The hardness proofs are the difficult part in this endeavor: We proceed by characterizing the reductions by the models and reduct models which the ASP programs should have, and then provide instantiations that meet the given specifications. Concerning the second question, we show that all relevant ASP reasoning tasks can be transformed into tasks over super-coherent programs, although this transformation is more of theoretical than practical interest.

Most recently, Answer Set Programming (ASP) has been attracting interest as a new paradigm for problem solving. An important aspect, for which several approaches have been presented, is the handling of preferences between rules. In this paper, we consider the problem of implementing preference handling approaches by means of meta-interpreters in Answer Set Programming. In particular, we consider the preferred answer set approaches by Brewka and Eiter, by Delgrande, Schaub and Tompits, and by Wang, Zhou and Lin. We present suitable meta-interpreters for these semantics using DLV, which is an efficient engine for ASP. Moreover, we also present a meta-interpreter for the weakly preferred answer set approach by Brewka and Eiter, which uses the weak constraint feature of DLV as a tool for expressing and solving an underlying optimization problem. We also consider advanced meta-interpreters, which make use of graph-based characterizations and often allow for more efficient computations. Our approach shows the suitability of ASP in general and of DLV in particular for fast prototyping. This can be fruitfully exploited for experimenting with new languages and knowledge-representation formalisms.

This paper focuses on computing general first-order parallel and prioritized circumscription with varying constants. We propose linear translations from general first-order circumscription to first-order theories under stable model semantics over arbitrary structures, including Tr_v for parallel circumscription and Tr^s_v for conjunction of parallel circumscriptions (further for prioritized circumscription). To improve the efficiency, we give an optimization \Gamma_{\exists} to reduce logic programs in size when eliminating existential quantifiers during the translations. Based on these results, a general first-order circumscription solver, named cfo2lp, is developed by calling answer set programming (ASP) solvers. Using circuit diagnosis problem and extended stable marriage problem as benchmarks, we compare cfo2lp with a propositional circumscription solver circ2dlp and an ASP solver with complex optimization metasp on efficiency. Experimental results demonstrate that for problems represented by first-order circumscription naturally and intuitively, cfo2lp can compute all solutions over finite structures. We also apply our approach to description logics with circumscription and repairs in inconsistent databases, which can be handled effectively.

In answer-set programming (ASP), the solutions of a problem are encoded in dedicated models, called answer sets, of a logical theory. These answer sets are computed from the program that represents the theory by means of an ASP solver and returned to the user as sets of ground first-order literals. As this type of representation is often cumbersome for the user to interpret, tools like ASPVIZ and IDPDraw were developed that allow for visualising answer sets. The tool Kara, introduced in this paper, follows these approaches, using ASP itself as a language for defining visualisations of interpretations. Unlike existing tools that position graphic primitives according to static coordinates only, Kara allows for more high-level specifications, supporting graph structures, grids, and relative positioning of graphical elements. Moreover, generalising the functionality of previous tools, Kara provides modifiable visualisations such that interpretations can be manipulated by graphically editing their visualisations. This is realised by resorting to abductive reasoning techniques using ASP itself. Kara is part of SeaLion, an integrated development environment (IDE) for ASP.

The goal of the Nurse Scheduling Problem is to find an assignment of nurses to shifts according to specific requirements. Frequently, a computed schedule may become not usable because of sudden absences of some nurses. In this cases, Nurse Rescheduling amounts to the computation of a new schedule, which has to satisfy the original requirements and the new absences. Additionally, a good solution to the Nurse Rescheduling Problem must be as similar as possible to the original schedule, which practically means that the number of changes has to be minimized. This paper focuses on the requirements specified by an Italian hospital, and recently addressed by an approach based on Answer Set Programming (ASP). Even if promising results have been obtained with ASP, the original encoding presents some intrinsic weaknesses, which are identified and eventually circumvented in this paper. The new encoding is designed by taking into account both intrinsic properties of Nurse Scheduling and internal details of ASP solvers, such as cardinality and weight constraint propagators. The performance gain of clingo and wasp is empirically verified on instances from ASP literature. As an additional contribution, the performance of clingo and wasp is compared to other declarative frameworks, namely SAT and ILP; the best performance is obtained by clingo running the new ASP encoding. The advanced ASP encodings are then extended to solve Nurse Rescheduling, and an empirical evaluation is conducted with clingo and wasp.

We introduce an answer-set semantics for abstract-constraint programs with disjunction in rule heads in the style of Faber, Leone, and Pfeifer (FLP). To this end, we extend the definition of an answer set for logic programs with aggregates in rule bodies using the usual FLP-reduct. Additionally, we also provide a characterisation of our semantics in terms of unfounded sets, likewise generalising the standard concept of an unfounded set. Our work is motivated by the desire to have simple and rule-based definitions of the semantics of an answer-set programming (ASP) language that is close to those implemented by the most prominent ASP solvers. The new definitions are intended as a theoretical device to allow for development methods and methodologies for ASP, e.g., debugging or testing techniques, that are general enough to work for different types of solvers. We use abstract constraints as an abstraction of literals whose truth values depend on subsets of an interpretation. This includes weight constraints, aggregates, and external atoms, which are frequently used in real-world answer-set programs. We compare the new semantics to previous semantics for abstract-constraint programs and show that they are equivalent to recent extensions of the FLP semantics to propositional and first-order theories when abstract-constraint programs are viewed as theories.

In recent years, Answer Set Programming has gained popularity as a viable paradigm for applications in knowledge representation and reasoning. This paper presents a novel methodology to compute answer sets of an answer set program. The proposed methodology maintains a bottom-up approach to the computation of answer sets (as in existing systems), but it makes use of a novel structuring of the computation, that originates from the non-ground version of the program. Grounding is lazily performed during the computation of the answer sets. The implementation has been realized using Constraint Logic Programming over finite domains.

We present some applications of intermediate logics in the field of Answer Set Programming (ASP). A brief, but comprehensive introduction to the answer set semantics, intuitionistic and other intermediate logics is given. Some equivalence notions and their applications are discussed. Some results on intermediate logics are shown, and applied later to prove properties of answer sets. A characterization of answer sets for logic programs with nested expressions is provided in terms of intuitionistic provability, generalizing a recent result given by Pearce. It is known that the answer set semantics for logic programs with nested expressions may select non-minimal models. Minimal models can be very important in some applications, therefore we studied them; in particular we obtain a characterization, in terms of intuitionistic logic, of answer sets which are also minimal models. We show that the logic G3 characterizes the notion of strong equivalence between programs under the semantic induced by these models. Finally we discuss possible applications and consequences of our results. They clearly state interesting links between ASP and intermediate logics, which might bring research in these two areas together.

Artificial intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union’s new General Data Protection Regulation tries to tackle this problem by stipulating a “right to explanation” for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is answer set programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question ofwhyan answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations, and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals isnotan answer set or why no solution exists at all.