Hybrid MKNF Knowledge Bases (HMKNF-KBs) constitute a formalism for tightly integrated reasoning over closed-world rules and open-world ontologies. This approach allows for accurate modeling of real-world systems, which often rely on both categorical and normative reasoning. Conflict-driven solving is the leading approach for computationally hard problems, such as satisfiability (SAT) and answer set programming (ASP), in which MKNF is rooted. This paper investigates the theoretical underpinnings required for a conflict-driven solver of HMKNF-KBs. The approach defines a set of completion and loop formulas, whose satisfaction characterizes MKNF models. This forms the basis for a set of nogoods, which in turn can be used as the backbone for a conflict-driven solver.

Answer set programming (ASP) is a popular declarative programming paradigm with various applications. Programs can easily have many answer sets that cannot be enumerated in practice, but counting still allows quantifying solution spaces. If one counts under assumptions on literals, one obtains a tool to comprehend parts of the solution space, so-called answer set navigation. However, navigating through parts of the solution space requires counting many times, which is expensive in theory. Knowledge compilation compiles instances into representations on which counting works in polynomial time. However, these techniques exist only for conjunctive normal form (CNF) formulas, and compiling ASP programs into CNF formulas can introduce an exponential overhead. This paper introduces a technique to iteratively count answer sets under assumptions on knowledge compilations of CNFs that encode supported models. Our anytime technique uses the inclusion–exclusion principle to improve bounds by over- and undercounting systematically. In a preliminary empirical analysis, we demonstrate promising results. After compiling the input (offline phase), our approach quickly (re)counts.

This paper introduces a framework for assisting policy authors in refining and improving their policies. In particular, we focus on authorization and obligation policies that can be encoded in Gelfond and Lobo’s $\mathscr{AOPL}$ language for policy specification. We propose a framework that detects the statements that make a policy inconsistent, underspecified, or ambiguous with respect to an action being executed in a given state. We also give attention to issues that arise at the intersection of authorization and obligation policies, for instance when the policy requires an unauthorized action to be executed. The framework is encoded in Answer Set Programming.

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language texts. Knowledge in a text is modeled using a Neo Davidsonian-like formalism, which is then represented as an answer set program. Relevant commonsense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model commonsense reasoning methods required to accomplish these tasks. In this paper, we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.

Dealing with context-dependent knowledge has led to different formalizations of the notion of context. Among them is the Contextualized Knowledge Repository (CKR) framework, which is rooted in description logics but links on the reasoning side strongly to logic programs and Answer Set Programming (ASP) in particular. The CKR framework caters for reasoning with defeasible axioms and exceptions in contexts, which was extended to knowledge inheritance across contexts in a coverage (specificity) hierarchy. However, the approach supports only this single type of contextual relation and the reasoning procedures work only for restricted hierarchies, due to nontrivial issues with model preference under exceptions. In this paper, we overcome these limitations and present a generalization of CKR hierarchies to multiple contextual relations, along with their interpretation of defeasible axioms and preference. To support reasoning, we use ASP with algebraic measures, which is a recent extension of ASP with weighted formulas over semirings that allows one to associate quantities with interpretations depending on the truth values of propositional atoms. Notably, we show that for a relevant fragment of CKR hierarchies with multiple contextual relations, query answering can be realized with the popular asprin framework. The algebraic measures approach is more powerful and enables, for example, reasoning with epistemic queries over CKRs, which opens interesting perspectives for the use of quantitative ASP extensions in other applications.

Automated commonsense reasoning (CR) is essential for building human-like AI systems featuring, for example, explainable AI. Event calculus (EC) is a family of formalisms that model CR with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g. time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

Decision support systems play an important role in medical fields as they can augment clinicians to deal more efficiently and effectively with complex decision-making processes. In the diagnosis of headache disorders, however, existing approaches and tools are still not optimal. On the one hand, to support the diagnosis of this complex and vast spectrum of disorders, the International Headache Society released in 1988 the International Classification of Headache Disorders (ICHD), now in its 3rd edition: a 200 pages document classifying more than 300 different kinds of headaches, where each is identified via a collection of specific nontrivial diagnostic criteria. On the other hand, the high number of headache disorders and their complex criteria make the medical history process inaccurate and not exhaustive both for clinicians and existing automatic tools. To fill this gap, we present head-asp, a novel decision support system for the diagnosis of headache disorders. Through a REST Web Service, head-asp implements a dynamic questionnaire that complies with ICHD-3 by exploiting two logical modules to reach a complete diagnosis while trying to minimize the total number of questions being posed to patients. Finally, head-asp is freely available on-line and it is receiving very positive feedback from the group of neurologists that is testing it.

In this paper we develop a concept aware multi-preferential semantics for dealing with typicality in description logics, where preferences are associated with concepts, starting from a collection of ranked TBoxes containing defeasible concept inclusions. Preferences are combined to define a preferential interpretation in which defeasible inclusions can be evaluated. The construction of the concept-aware multipreference semantics is related to Brewka’s framework for qualitative preferences. We exploit Answer Set Programming (in particular, asprin) to achieve defeasible reasoning under the multipreference approach for the lightweight description logic ξ$\mathcal L_ \bot ^ + $.

Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios.

Powerful formalisms for abstract argumentation have been proposed, among them abstract dialectical frameworks (ADFs) that allow for a succinct and flexible specification of the relationship between arguments and the GRAPPA framework which allows argumentation scenarios to be represented as arbitrary edge-labeled graphs. The complexity of ADFs and GRAPPA is located beyond NP and ranges up to the third level of the polynomial hierarchy. The combined complexity of Answer Set Programming (ASP) exactly matches this complexity when programs are restricted to predicates of bounded arity. In this paper, we exploit this coincidence and present novel efficient translations from ADFs and GRAPPA to ASP. More specifically, we provide reductions for the five main ADF semantics of admissible, complete, preferred, grounded, and stable interpretations, and exemplify how these reductions need to be adapted for GRAPPA for the admissible, complete, and preferred semantics.

Answer Set Programming (ASP) is a logic programming paradigm featuring a purely declarative language with comparatively high modeling capabilities. Indeed, ASP can model problems in NP in a compact and elegant way. However, modeling problems beyond NP with ASP is known to be complicated, on the one hand, and limited to problems in $\[\Sigma _2^P\]$ on the other. Inspired by the way Quantified Boolean Formulas extend SAT formulas to model problems beyond NP, we propose an extension of ASP that introduces quantifiers over stable models of programs. We name the new language ASP with Quantifiers (ASP(Q)). In the paper we identify computational properties of ASP(Q); we highlight its modeling capabilities by reporting natural encodings of several complex problems with applications in artificial intelligence and number theory; and we compare ASP(Q) with related languages. Arguably, ASP(Q) allows one to model problems in the Polynomial Hierarchy in a direct way, providing an elegant expansion of ASP beyond the class NP.

This paper explores the use of Answer Set Programming (ASP) in solving Distributed Constraint Optimization Problems (DCOPs). The paper provides the following novel contributions: (1) it shows how one can formulate DCOPs as logic programs; (2) it introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (3) it experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative programming counterpart) as well as solve some problems that DPOP fails to solve, due to memory limitations; and (4) it demonstrates the applicability of ASP in a wide array of multi-agent problems currently modeled as DCOPs.

Consistent query answering over a database that violates primary key constraints is a classical hard problem in database research that has been traditionally dealt with logic programming. However, the applicability of existing logic-based solutions is restricted to data sets of moderate size. This paper presents a novel decomposition and pruning strategy that reduces, in polynomial time, the problem of computing the consistent answer to a conjunctive query over a database subject to primary key constraints to a collection of smaller problems of the same sort that can be solved independently. The new strategy is naturally modeled and implemented using Answer Set Programming (ASP). An experiment run on benchmarks from the database world prove the effectiveness and efficiency of our ASP-based approach also on large data sets.

The impact of toxic outbreaks of Gymnodinium catenatum, producer of paralytic shellfish poisoning toxins (PSP toxins) and Pseudonitzschia spp., producer of amnesic shellfish poisoning toxins (ASP toxins), was studied throughout the culture cycle of the king scallop Pecten maximus (length from 40 to 100 mm). Toxin concentration, in plankton and in the digestive gland of the scallops, other variables that describe the state of the cultured scallops (mortality, growth, length/weight ratio, gonadosomatic index) and the main environmental conditions (temperature and in vivo fluorescence) were determined from December 2001 to April 2003. Temperature ranged from 14 °C during the winter to 22 °C at the end of July. In vivo fluorescence ranged from not detectable to 25 relative units, with a maximum also recorded at the end of July. ASP toxin levels, in both plankton and organisms, were high during the first half of the sampling period (maximum 450 ng domoic acid L−1, in plankton, and 175 µg·g−1 of scallop digestive gland) when the scallops were juvenile. PSP toxins attained larger concentrations in the second half of the sampling period (maximum of 1.8 nmol L−1 in plankton and 550 nmol g−1 of digestive gland, of gonyautoxins after hydrolysis). Several mortality events took place during the period studied, but none of them coincided neither with an ASP or PSP outbreak nor with long exposure of the scallops to these toxins. One of the recorded mortalities occurred simultaneously with a high concentration of PSP in the scallops, but also with an important spawning. Additionally, it was not coincidental with the bloom of toxic plankton, thus, post-spawning stress seemed to be the main responsible for the mortality. No appreciable effect of ASP or PSP toxinson growth or reproductive state of the cultured scallops was found. Therefore, neither ASP nor PSP toxins appeared to represent a direct threat to the culture of Pecten maximus in that case.

Aminotransferase (AT) activity against 18 amino acids was studied in ten strains of three species of Lactobacillus. A method for permeabilisation of cells was developed using toluene and ethanol combined with mechanical treatment. It was found that the AT activities in the washed permeabilised cells (W-PC) corresponded well to that in cell-free extracts (CFE). The AT specificity pattern was species as well as strain dependant. Strains of Lb. helveticus had high specificity for aromatic amino acids (ArAA) and lower activity against branched-chain amino acids (BcAA) and Asp, while strains of Lb. paracasei subsp. paracasei degraded BcAA and Asp, but had a lower and variable specificity against ArAA. One of the Lb. paracasei strains was characterised by having very high AT activity against all three BcAA (Ile, Leu, Val) compared with any of the other Lb. paracasei strains tested. Strains of Lb. danicus, which is a newly discovered Lactobacillus species isolated from cheese, had up to about 20 times higher AT activity against Leu than Lb. paracasei and Lb. helveticus. The permeabilised cells of Lb. danicus had also considerably higher AT activity against ArAA than Lb. paracasei and Lb. helveticus strains, and also higher AT activity against Asp. All Lactobacillus strains tested had AT activity against Met, but at a much lower rate than against other amino acids. Results of this study also demonstrated a chemical reaction between α-ketoglutaric acid and Asp that was catalysed by pyridoxal-5-phosphate without any AT present.

Domoic acid is a neurotoxin responsible for the Amnesic Shellfish Poisoning (ASP). With the aim of determining its depuration kinetics in raft mussels Mytilus galloprovincialis and the effects of body weight, salinity and temperature on it, an experiment involving these factors was carried out. Mussels which had incorporated this toxin to a concentration of 153 μg·g–1 of soft tissue during a bloom of the pennate diatom Pseudo nitzschia australis, were collected from the Galician Rías and placed at temperatures of 18 and 22 ºC and salinities of 12.5 and 31. Mussel samples were taken at the start of the experiment and daily during the four subsequent days and the domoic acid contents of the soft tissues were analysed. The simple one-compartment kinetics, that is a kinetics in which all toxin depurates at the same rate, was fitted to the data, with good quantitative results. Notwithstanding, the deviations of the model from the actual data were dependent on time, suggesting that such a simple model is not enough to correctly describe the data, and that a more complex kinetics may be more adequate. A two-compartment kinetic model, in which two pools of toxins exist (compartments), each one depurating at different rate, described qualitatively better the depuration but its quantitative contribution to the fit was not statistically significant. The parameters of the model, obtained by least squares fitting, suggest the possibility of a small second compartment of very small or null depuration rate, as detected in other species. This kind of model would explain the reduced quantitative contribution of the second (slowly depurating) compartment.Low salinity was shown to reduce the depuration rate. The two other factors checked, temperature and body weight, nor any interaction had significant effect on depuration rate.

Hookworms are gut-dwelling, blood-feeding nematodes that infect hundreds of millions of people, particularly in the tropics. As part of a program aiming to define novel drug targets and vaccine candidates for human parasitic nematodes, genes expressed in adults of the human hookworm Necator americanus were surveyed by the expressed sequence tag approach. In total 161 new hookworm genes were identified. For the majority of these, a function could be assigned by homology. The dataset includes proteases, protease inhibitors, a lipid binding protein, C-type lectins, an anti-bacterial factor, globins and other genes of interest from a drug or vaccine development viewpoint. Three different classes of small, secreted proteins were identified that may be involved in the host–parasite interaction, including potential potassium channel blocking peptides. One third of the genes were novel. These included highly expressed, secreted (glyco)proteins which may be part of the excretory–secretory products of these important pathogens. Of particular interest are a family of 9 genes with similarity to the immunomodulatory protein, neutrophil inhibitory factor, that may play a role in establishing an immunocompromised niche for this successful parasite.

We describe a new computational technique to predict conformationally switching elements in proteins from their amino acid sequences. The method, called ASP (Ambivalent Structure Predictor), analyzes results from a secondary structure prediction algorithm to identify regions of conformational ambivalence. ASP identifies ambivalent regions in 16 test protein sequences for which function involves substantial backbone rearrangements. In the test set, all sites previously described as conformational switches are correctly predicted to be structurally ambivalent regions. No such regions are predicted in three negative control protein sequences. ASP may be useful as a guide for experimental studies on protein function and motion in the absence of detailed three-dimensional structural data.

The sequences of several members of the myosin family of molecular motors are evaluated using ASP (Ambivalent Structure Predictor), a new computational method. ASP predicts structurally ambivalent sequence elements by analyzing the output from a secondary structure prediction algorithm. These ambivalent sequence elements form secondary structures that are hypothesized to function as switches by undergoing conformational rearrangement. For chicken skeletal muscle myosin, 13 discrete structurally ambivalent sequence elements are identified. All 13 are located in the heavy chain motor domain. When these sequence elements are mapped into the myosin tertiary structure, they form two compact regions that connect the actin binding site to the adenosine 5′-triphosphate (ATP) site, and the ATP site to the fulcrum site for the force-producing bending of the motor domain. These regions, predicted by the new algorithm to undergo conformational rearrangements, include the published known and putative switches of the myosin motor domain, and they form plausible allosteric connections between the three main functional sites of myosin. The sequences of several other members of the myosin I and II families are also analyzed.