In this paper we present a successful application of logic programming for e-tourism: the iTravel system. The system exploits two technologies that are based on the state-of-the-art computational logic system DLV: (i)...
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In this paper we present a successful application of logic programming for e-tourism: the iTravel system. The system exploits two technologies that are based on the state-of-the-art computational logic system DLV: (i) a system for ontology representation and reasoning, called OntoDLV;and, (ii) HlLeX a semantic information-extraction tool. The core of iTravel is an ontology which models the domain of tourism offers. The ontology is automatically populated by extracting the information contained in the tourism leaflets produced by tour operators. A set of specifically devised logic programs is used to reason on the information contained in the ontology for selecting the holiday packages that best fit the customer needs. An intuitive web-based user interface eases the task of interacting with the system for both the customers and the operators of a travel agency.
Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logic...
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Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of multilayer perceptrons (MLPs). In this paper we consider weighted conditional ALC knowledge bases with typicality in the finitely many-valued case, through three different semantic constructions. For the boolean fragment LC of ALC we exploit answer set programming and asprin for reasoning with the concept-wise multipreference entailment under a phi-coherent semantics, suitable to characterize the stationary states of MLPs. As a proof of concept, we experiment the proposed approach for checking properties of trained MLPs.
Epistemic logic programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets, that is, syntactically,...
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Epistemic logic programs (ELPs), extend answer set programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets, that is, syntactically, sets of sets of atoms. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property, that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to "traditional" ASP. We analyze the possibility of changing the perspective, shifting from a bottom-up to a top-down approach to splitting. We propose a basic top-down approach, which we prove to be equivalent to the bottom-up one. We then propose an extended approach, where our new definition: (i) is provably applicable to many of the existing semantics;(ii) operates similarly to "traditional" ASP;(iii) provably coincides under any semantics with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified);(iv) better adheres to common ASP programming methodology.
Strong equivalence is an important concept in the theory of answer set programming. Informally speaking, two sets of rules are strongly equivalent if they have the same meaning in any context. Equilibrium logic was us...
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Strong equivalence is an important concept in the theory of answer set programming. Informally speaking, two sets of rules are strongly equivalent if they have the same meaning in any context. Equilibrium logic was used to prove that sets of rules expressed as propositional formulas are strongly equivalent if and only if they are equivalent in the logic of here-and-there. We extend this line of work to formulas with infinitely long conjunctions and disjunctions, show that the infinitary logic of here-and-there characterizes strong equivalence of infinitary formulas, and give an axiomatization of that logic. This is useful because of the relationship between infinitary formulas and logic programs with local variables. (C) 2017 Elsevier B.V. All rights reserved.
answer set programming (ASP) is a well-known declarative formalism in logic programming. Efficient implementations made it possible to apply ASP in many scenarios, ranging from deductive databases applications to the ...
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answer set programming (ASP) is a well-known declarative formalism in logic programming. Efficient implementations made it possible to apply ASP in many scenarios, ranging from deductive databases applications to the solution of hard combinatorial problems. State-of-the-art ASP systems are based on the traditional ground&solve approach and are general-purpose implementations, i.e., they are essentially built once for any kind of input program. In this paper, we propose an extended architecture for ASP systems, in which parts of the input program are compiled into an ad-hoc evaluation algorithm (i.e., we obtain a specific binary for a given program), and might not be subject to the grounding step. To this end, we identify a condition that allows the compilation of a sub-program, and present the related partial compilation technique. Importantly, we have implemented the new approach on top of a well-known ASP solver and conducted an experimental analysis on publicly-available benchmarks. Results show that our compilation-based approach improves on the state of the art in various scenarios, including cases in which the input program is stratified or the grounding blow-up makes the evaluation unpractical with traditional ASP systems.
When people communicate, we often face situations where decisions have to be made, regardless of silence of one of the interlocutors. That is, we have to decide from incomplete information, guessing the intentions of ...
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When people communicate, we often face situations where decisions have to be made, regardless of silence of one of the interlocutors. That is, we have to decide from incomplete information, guessing the intentions of the silent person. Implicatures allow to make inferences from what is said, but we can also infer from omission, or specifically from intentional silence in a conversation. In some contexts, not saying p generates a conversational implicature: that the speaker did not have sufficient reason, all things considered, to say p. This behaviour has been studied by several disciplines but barely touched in logic or artificial intelligence. After reviewing some previous studies of intentional silence and implicature, we formulate a semantics with five different interpretations of omissive implicature, in terms of the Says() predicate, and focus on puzzles involving assertions or testimonies, to analyze their implications. Several conclusions are derived from the different possibilities that were opened for analysis after taking into account silence. Finally, we develop a general strategy for the use of the proposed semantics in cases involving some kind of silence.
Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefo...
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Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use answer set programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved.
One main characteristic of virtual enterprises are short-term collaborations between business partners to provide efficient and individualized services to customers. The MOVE project targets at a methodology and a sof...
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One main characteristic of virtual enterprises are short-term collaborations between business partners to provide efficient and individualized services to customers. The MOVE project targets at a methodology and a software framework to support such flexible collaborations based on process oriented design and communication by Web services. MOVE framework supports the graphical design and verification of business processes, the execution and supervision of processes in transaction-oriented environment, and the dynamic composition and optimization of processes. A business process may be composed from a set of Web services, deployed itself as Web service and executed in the framework. The composition of processes from Web services is implemented with methods from A I-planning. We apply answer set programming (ASP) and map Web service descriptions and customer requests into the input language of the ASP software DLV. Composition goals and constraints guide a composition challenge. We show the performance of our program and give some implementation details. Finally we conclude with some insights.
Unsatisfiable core analysis can boost the computation of optimum stable models for logic programs with weak constraints. However, current solvers employing unsatisfiable core analysis either run to completion, or prov...
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Unsatisfiable core analysis can boost the computation of optimum stable models for logic programs with weak constraints. However, current solvers employing unsatisfiable core analysis either run to completion, or provide no suboptimal stable models but the one resulting from the preliminary disjoint cores analysis. This drawback is circumvented here by introducing a progression based shrinking of the analyzed unsatisfiable cores. In fact, suboptimal stable models are possibly found while shrinking unsatisfiable cores, hence resulting into an anytime algorithm. Moreover, as confirmed empirically, unsatisfiable core analysis also benefits from the shrinking process in terms of solved instances.
In spite of the improvements in the performance of many solvers for model-based languages, it is still possible for the search algorithm to focus on the wrong areas of the search space, preventing the solver from retu...
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In spite of the improvements in the performance of many solvers for model-based languages, it is still possible for the search algorithm to focus on the wrong areas of the search space, preventing the solver from returning a solution in an acceptable amount of time. This prospect is a real concern e. g. in an industrial setting, where users typically expect consistent performance. To overcome this problem, we propose a framework that allows learning and using domain-specific heuristics in the solvers. The learning is done offline, on representative instances from the target domain, and the learned heuristics are then used for choice-point selection. In this paper we focus on answer set programming (ASP) solvers. In our experiments, the introduction of domain-specific heuristics improved performance quite substantially on hard instances, and in particular made overall performance more consistent by reducing the number of cases in which the solver timed out.
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