An Answer Set Programming Approach to Argumentative Reasoning in the ASPIC+ Framework

The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

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.

The Nuclear Medicine Scheduling (NMS) problem consists of assigning patients to a day, on which the patient will undergo the medical check, the preparation and the actual image detection process. The schedule should consider the different requirements of the patients and the available resources, e.g. varying time required for different diseases and radiopharmaceuticals used, number of injection chairs and tomographs available. In this paper, we present two solutions to the NMS problem based on Answer Set Programming (ASP). The first solution is a direct ASP encoding, which is then processed by an ASP solver, while the second solution employs a Logic-based Bender Decomposition (LBBD) approach implemented through the usage of multi-shot solving. Experiments employing real data show that the direct encoding provides overall satisfying results in terms of solutions quality in a relatively short time, and that the LBBD approach also helps in improving scalability.

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.

In order to enable a secure interaction between dynamically discovered software services and the client’s application in a cooperative information system such as service oriented system, one of the pre-requisites is the reconciliation of service-specific security policies of all stakeholders. Existing service discovery research does not address the issue of enormous search space in finding security-aware services based on preferred security policy alternatives of the client of software services. In this paper, we propose an answer set programming (ASP) approach, drawn from the field of artificial intelligence (AI), to explore a viable solution of finding security-aware services for the client. We argue that the ASP approach can significantly reduce the search space and achieve great performance gains. We use ASP to: (i) specify security policies including expressing service-specific security preference weighting and importance scoring in quantifiable terms; and (ii) reason about the compliance between the security policies of the client and the software service.

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.

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.

We present a prototypical system, plasp, implementing Planning by compilation to Answer Set Programming (ASP). Our approach is inspired by Planning as Satisfiability, yet it aims at keeping the actual compilation simple in favor of modeling planning techniques by meta-programming in ASP. This has several advantages. First, ASP modelings are easily modifiable and can be studied in a transparent setting. Second, we can take advantage of available ASP grounders to obtain propositional representations. Third, we can harness ASP solvers providing incremental solving mechanisms. Finally, the ASP community gains access to a wide range of planning problems, and the planning community benefits from the knowledge representation and reasoning capacities of ASP.KeywordsPlanning ProblemOperator SplittingConcurrent ActionPropositional RepresentationAutomate PlanningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Geographical information systems are one of the most important application areas of belief revision. Recently, Würbel and colleagues [32] have applied the so-called ”removed sets revision” (RSR) to the problem of assessment of water heights in a flooded valley. The application was partially satisfactory since only a small part of the valley has been handled. This paper goes one step further, and proposes an extension of (RSR) called ”Prioritized Removed Sets Revision” (PRSR). We show that (PRSR) performed using answer set programming makes possible to solve a practical revision problem provided by a real application in the framework of geographical information system (GIS). We first show how PRSR can be encoded into a logic program with answer set semantics, we then present an adaptation of the smodels system devoted to efficiently compute the answer sets in order to perform PRSR. The experimental study shows that the answer set programming approach gives better results than previous implementations of RSR and in particular it allows to handle the whole valley. Lastly, some experimental studies comparing our encoding with implementations based on SAT-solvers are also provided.

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.

Answer Set Programming (ASP) is a declarative programming paradigm targeted to solving search problems. The basic idea of ASP is similar to, for example, SAT-based planning or constraint satisfaction problems but ASP provides a more powerful knowledge representation language for effective problem encoding. A number of successful ASP systems have already been developed and applied in a large range of areas. The talk explains the theoretical underpinnings of ASP, introduces the answer set programming paradigm, outlines computational techniques used in current ASP solvers, and discusses some interesting applications of the approach.

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.

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.

The recent application of Machine Learning techniques to the Answer Set Programming (ASP) field proved to be effective. In particular, the multi-engine ASP solver ME-ASP is efficient: it is able to solve more instances than any other ASP system that participated to the 3rd ASP Competition on the System Track benchmarks. In the ME-ASP approach, classification methods inductively learn off-line algorithm selection policies starting from both a set of features of instances in a training set, and the solvers performance on such instances. In this paper we present an improvement to the multi-engine framework of ME-ASP , in which we add the capability of updating the learned policies when the original approach fails to give good predictions. An experimental analysis, conducted on training and test sets of ground instances obtained from the ones submitted to the System Track of the 3rd ASP Competition, shows that the policy adaptation improves the performance of ME-ASP when applied to test sets containing domains of instances that were not considered for training.