answer set programming (ASP) is a well-known declarative problem solving paradigm developed in the field of nonmonotonic reasoning and logic programming. The usual target of ASP is the solution of combinatorial search...
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answer set programming (ASP) is a well-known declarative problem solving paradigm developed in the field of nonmonotonic reasoning and logic programming. The usual target of ASP is the solution of combinatorial search problems, nonetheless the language of ASP was extended with weak constraints for concise modelling of optimization problems. In the case of ASP programs with weak constraints, the main computational task of an ASP solver is optimum stable model search. In this article, we present and compare several algorithms for optimum stable model search. We consider solutions traditionally adopted by ASP solvers, and we introduce new solving strategies obtained by porting to the ASP setting some algorithms that were introduced for Maximum Satisfiability solving. The article also reports on the implementation of these algorithms in the ASP solver WASP. An empirical analysis highlights pros and cons of different strategies for computing optimum stable models.
Combining Description Logic (DL) ontologies and nonmonotonic rules has gained increasing attention in the past decade, due to the growing range of applications of DLs. A well-known proposal for such a combination are ...
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Combining Description Logic (DL) ontologies and nonmonotonic rules has gained increasing attention in the past decade, due to the growing range of applications of DLs. A well-known proposal for such a combination are non-monotonic DL-programs, which support rule based reasoning on top of DL ontologies in a loose coupling, using a well-defined query interface. However, inconsistency may easily arise as a result of the interaction of the rules and the ontology, such that no answerset (i.e., model) of a DL-program exists;this makes the program useless. To overcome this problem, we present a framework for repairing inconsistencies in DL-programs by exchanging formulas of an ontology formulated DL-Lite(A), which is a prominent DL that allows for tractable reasoning. Viewing the data part of the ontology as a source of inconsistency, we define program repairs and repair answersets based on them. We analyze the complexity of the notion, and we extend an algorithm for evaluating DL-programs to compute repair answersets, under optional selection of preferred repairs that satisfy additional constraints. The algorithm induces a generalized ontology repair problem, in which the entailment respectively non-entailment of queries to the ontology, subject to possible updates, must be achieved by a data change. While this problem is intractable in general, we identify several tractable classes of preferred repairs that are useful in practice. For the class of deletion repairs among them, we optimize the algorithm by reducing query evaluation to constraint matching, based on the novel concept of support set, which roughly speaking is a portion of the data from which entailment of an ontology query follows. Our repair approach is implemented within an answerset program system, using a declarative method for repair computation. An experimental evaluation on a suite of benchmark problems shows the effectiveness of our approach and promising results, both regarding performance and qu
answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain ...
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answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks.
In temporal extensions of answer set programming (ASP) based on linear time, the behavior of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts aw...
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In temporal extensions of answer set programming (ASP) based on linear time, the behavior of dynamic systems is captured by sequences of states. While this representation reflects their relative order, it abstracts away the specific times associated with each state. However, timing constraints are important in many applications like, for instance, when planning and scheduling go hand in hand. We address this by developing a metric extension of linear-time temporal equilibrium logic, in which temporal operators are constrained by intervals over natural numbers. The resulting Metric Equilibrium Logic (MEL) provides the foundation of an ASP-based approach for specifying qualitative and quantitative dynamic constraints. To this end, we define a translation of metric formulas into monadic first-order formulas and give a correspondence between their models in MEL and Monadic Quantified Equilibrium Logic, respectively. Interestingly, our translation provides a blue print for implementation in terms of ASP modulo difference constraints.
In the last few years, microprocessor technologies have been moving towards multi-core architectures, in order to improve performance as well as reduce power consumption. This makes real Symmetric MultiProcessing (SMP...
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In the last few years, microprocessor technologies have been moving towards multi-core architectures, in order to improve performance as well as reduce power consumption. This makes real Symmetric MultiProcessing (SMP) available even on nondedicated machines, and paves the way to the development of better performing software. Notably, the recent application of answer set programming (ASP) in different emerging areas, such as knowledge management or information extraction/integration, shows that performance is a crucial issue also for ASP systems. Among the tasks performed by such systems, the instantiation process, which consists of generating a variable-free program equivalent to the input one, is one of the most expensive from a computational viewpoint, especially in the case of huge input data. In this paper a new strategy exploiting parallelism for the instantiation of ASP programs is proposed. An implementation of this strategy and its integration with the grounding module of the DLV system is discussed. The results of an experimental analysis are also presented, which confirm that the strategy is effective in making ASP instantiation more efficient. (c) 2008 Elsevier Inc. All fights reserved.
Day by day, malware as a service becomes more popular and easy to acquire, thus allowing anyone to start an attack without any technical background, which in turn introduces challenges for detecting such attacks. One ...
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Day by day, malware as a service becomes more popular and easy to acquire, thus allowing anyone to start an attack without any technical background, which in turn introduces challenges for detecting such attacks. One of those challenges is the detection of malware activities early to prevent harm as much as possible. This paper presents a trusted dynamic analysis approach based on answer set programming (ASP), a logic engine inference named Malware-Logic-Miner (MalpMiner). ASP is a nonmonotonic reasoning engine built on an open-world assumption, which allows MalpMiner to adopt commonsense reasoning when capturing malware activities of any given binary. Furthermore, MalpMiner requires no prior training;therefore, it can scale up quickly to include more malware-attack attributes. Moreover, MalpMiner considers the invoked application programming interfaces' values, resulting in correct malware behaviour modelling. The baseline experiments prove the correctness of MalpMiner related to recognizing malware activities. Moreover, MalpMiner achieved a detection ratio of 99% with a false-positive rate of less than 1% while maintaining low computational costs and explaining the detection decision.
We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuro...
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We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child's performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments.
Disjunctive logic programming (DLP) is a very expressive formalism. It allows for expressing every property of finite structures that is decidable in the complexity class Sigma(P)(2) (=NPNP). Despite this high express...
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Disjunctive logic programming (DLP) is a very expressive formalism. It allows for expressing every property of finite structures that is decidable in the complexity class Sigma(P)(2) (=NPNP). Despite this high expressiveness, there are some simple properties, often arising in real-world applications, which cannot be encoded in a simple and natural manner. Especially properties that require the use of arithmetic operators (like sum, times, or count) on a set or multiset of elements, which satisfy some conditions, cannot be naturally expressed in classic DLP. To overcome this deficiency, we extend DLP by aggregate functions in a conservative way. In particular, we avoid the introduction of constructs with disputed semantics, by requiring aggregates to be stratified. We formally define the semantics of the extended language (called DLPA), and illustrate how it can be profitably used for representing knowledge. Furthermore, we analyze the computational complexity of DLPA, showing that the addition of aggregates does not bring a higher cost in that respect. Finally, we provide an implementation of DLPA in DLV-a state-of-the-art DLP system-and report on experiments which confirm the usefulness of the proposed extension also for the efficiency of computation.
NP-SPEC is a language for specifying problems in NP in a declarative way. Despite the fact that the semantics of the language was given by referring to Datalog with circumscription, which is very close to answerset p...
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NP-SPEC is a language for specifying problems in NP in a declarative way. Despite the fact that the semantics of the language was given by referring to Datalog with circumscription, which is very close to answer set programming (ASP), so far the only existing implementations are by means of (ECLPSe)-P-i Prolog and via Boolean satisfiability solvers. In this paper, we present translations from NP-SPEC to ASP, and provide an experimental evaluation of existing implementations and the proposed translations into ASP using various ASP solvers. The results show that translating into ASP clearly has an edge over the existing translation into SAT, which involves an intrinsic grounding process. We also argue that it might be useful to incorporate certain language constructs of NP-SPEC into mainstream ASP.
Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solv...
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Stream reasoning systems are designed for complex decision-making from possibly infinite, dynamic streams of data. Modern approaches to stream reasoning are usually performing their computations using stand-alone solvers, which incrementally update their internal state and return results as the new portions of data streams are pushed. However, the performance of such approaches degrades quickly as the rates of the input data and the complexity of decision problems are growing. This problem was already recognized in the area of stream processing, where systems became distributed in order to allocate vast computing resources provided by clouds. In this paper we propose a distributed approach to stream reasoning that can efficiently split computations among different solvers communicating their results over data streams. Moreover, in order to increase the throughput of the distributed system, we suggest an interval-based semantics for the LARS language, which enables significant reductions of network traffic. Performed evaluations indicate that the distributed stream reasoning significantly outperforms existing stand-alone LARS solvers when the complexity of decision problems and the rate of incoming data are increasing.
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