26 research outputs found

    Three-valued completion for abductive logic programs

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    A characterization of stable models using a non-monotonic operator

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    Proving deadlock freedom of logic programs with dynamic scheduling

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    In increasingly many logic programming systems, the Prolog left to right selection rule has been replaced with dynamic selection rules, that select an atom of a query among those satisfying suitable conditions. These conditions describe the form of the arguments of every program predicate, by means of a so-called delay declaration. Dynamic selection rules introduce the possibility of deadlock, an abnormal form of termination that occurs if the query is non-empty and it contains no `selectable' atoms. In this paper, we introduce a simple compositional assertional method for proving deadlock freedom. The method is based on the notion of suspension cover, a static description of the possible dynamic schedulings of the body atoms of a clause, according to a given delay declaration. In the method, we assume that monotonic assertions are used for specifying the conditions of the delay declaration. Apart sections are devoted to two more practical instances of the method, that use types and modes, respectively

    Proving termination of logic programs with delay declarations

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    In this paper we propose a method for proving termination of logic programs with delay declarations. The method is based on the notion of recurrent logic program, which is used to prove programs terminating wrt an arbitrary selection rule. Most importantly, we use the notion of bound query (as proposed by M. Bezem) in the definition of cover, a new notion which forms the kernel of our approach. We introduce the class of delay recurrent programs and prove that programs in this class terminate for all local delay selection rules, provided that the delay conditions imply boundedness. The corresponding method can be also used to transform a logic program into a terminating logic program with delay declarations

    Comparing negation in logic programming and in Prolog

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    We compare here two uses of negation -- in logic programming and in Prolog. As in Prolog negation is defined by means of meta-programming facilities and the cut operator, this requires a careful reexamination of the assumptions about the underlying syntax and a precise definition of the computational processes involved. After taking care of these matters we establish a formal result showing an equivalence in appropriate sense between these two uses of negation. This result allows us to argue about correctness of various known Prolog programs which use negation by reasoning about the corresponding general logic programs

    Comparing negation in logic programming and in Prolog

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    Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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    An amendment to this paper has been published and can be accessed via a link at the top of the paper.(undefined)info:eu-repo/semantics/publishedVersio

    SBML Level 3: an extensible format for the exchange and reuse of biological models

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    Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution
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