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Cybersecurity policy verification with declarative programming

APPLIED INTELLIGENCE 2016年第1期45卷 83-95页

作者： Hewett, Rattikorn Kijsanayothin, Phongphun Bak, Stephanie Galbrei, Marry Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Naresuan Univ Dept Elect & Comp Engn Phitsanulok Thailand Cornell Univ Dept Comp Sci Itica NY USA Nazarene Univ Dept Phys Bourbonnais IL USA

Cybersecurity is a growing concern in today's society. Security policies have been developed to ensure that data and assets remain protected for legitimate users, but there must be a mechanism to verify that these policies can be enforced. This paper addresses the verification problem of security policies in role-based access control of enterprise software. Most existing approaches employ traditional logic or procedural programming that tends to involve complex expressions or search with backtrack. These can be time-consuming, and hard to understand, and update, especially for large-scale security verification problems. Declarative programming paradigms such as "answer set" programming have been widely used to alleviate these issues by ways of elegant and flexible modeling for complex search problems. However, solving problems using these paradigms can be challenging due to the nature and limitation of the declarative problem solver. This paper presents an approach to automated security policy verification using answer set programming. In particular, we investigate how the separation of duty security policy in role-based access control can be verified. Our contribution is a modeling approach that maps this verification problem into a graph-coloring problem to facilitate the use of generate-and-test in a declarative problem-solving paradigm. The paper describes a representation model and rules that drive the answer set Solver and illustrates the proposed approach to securing web application software to assist the hiring process in a company.

关键词： answer set programming Security constraints Cybersecurity

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Directed random walks and constraint programming reveal active pathways in hepatocyte growth factor signaling

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FEBS JOURNAL 2016年第2期283卷 350-360页

作者： Kittas, Aristotelis Delobelle, Aurelien Schmitt, Sabrina Breuhahn, Kai Guziolowski, Carito Grabe, Niels UCL Dept Chem Engn Ctr Proc Syst Engn London WC1E 7JE England CNRS UMR 6597 IRCCyN Ecole Ctr Nantes F-75700 Paris France Univ Heidelberg Hosp Inst Pathol Heidelberg Germany Univ Heidelberg Hosp NCT Dept Med Oncol Heidelberg Germany

An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and answer set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: https://***/akittas/biosubg.

关键词： answer set programming biological networks HGF signaling logic programming logical modelling random\walks subgraph extraction

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Expressiveness of Logic Programs under the General Stable Model Semantics

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2017年第2期18卷 9-9页

作者： Zhang, Heng Zhang, Yan Tianjin Univ Sch Comp Software Tianjin 300072 Peoples R China Western Sydney Univ Sch Comp Engn & Math Locked Bag 1797 Penrith NSW 2751 Australia Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China

Stable model semantics had been recently generalized to non-Herbrand structures by several works, which provides a unified framework and solid logical foundations for answer set programming. This article focuses on the expressiveness of normal and disjunctive logic programs under general stable model semantics. A translation from disjunctive logic programs to normal logic programs is proposed for infinite structures. Over finite structures, some disjunctive logic programs are proved to be intranslatable to normal logic programs if the arities of auxiliary predicates and functions are bounded in a certain way. The equivalence of the expressiveness of normal logic programs and disjunctive logic programs over arbitrary structures is also shown to coincide with that over finite structures and coincide with whether the complexity class NP is closed under complement. Moreover, to obtain a more explicit picture of the expressiveness, some intertranslatability results between logic program classes, and fragments of second-order logic are established.

关键词： Languages Theory answer set programming expressiveness complexity nonmonotonic reasoning second-order logic

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Representing the Language of the Causal Calculator in answer set programming

Representing the Language of the Causal Calculator in Answer...

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作者： Casolary, Michael Arizona State University

学位级别：M.S.

Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.

关键词： Computer Science Artificial Intelligence answer set programming asp causal logic ccalc cplus2asp knowledge representation

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Giving Hints For Logic programming Examples Without Revealing Solutions 24

Giving Hints For Logic Programming Examples Without Revealin...

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24th Signal Processing and Communication Application Conference (SIU)

作者： Avci, Gokhan Mehuljic, Mustafa Schuller, Peter Marmara Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781509016792

We introduce a framework for supporting learning to program in the paradigm of answer set programming (ASP), which is a declarative logic programming formalism. Based on the idea of teaching by asking the student to complete small example ASP programs, we introduce a three-stage method for giving hints to the student without revealing the correct solution of an example. We categorize mistakes into (i) syntactic mistakes, (ii) unexpected but syntactically correct input, and (iii) semantic mistakes, describe mathematical definitions of these mistakes, and show how to compute hints from these definitions.

关键词： logic programming answer set programming computer aided learning

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ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks

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ALGORITHMS FOR MOLECULAR BIOLOGY 2017年第1期12卷 20-20页

作者： Ben Abdallah, Emna Folschette, Maxime Roux, Olivier Magnin, Morgan Ecole Cent Nantes CNRS LS2N UMR 6004 1 Rue Noe F-44321 Nantes France Univ Nantes CNRS LS2N UMR 6004 1 Rue Noe F-44321 Nantes France Univ Nice Sophia Antipolis CNRS UMR 7271 I3S 2000 Route Lucioles F-06900 Nice France Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan

Background: This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins, ...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. Results: We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. Conclusion: The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all

关键词： Biological regulatory network Multiple-valued networks Attractors Steady states Cycles answer set programming

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Modeling and Language Extensions

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AI MAGAZINE 2016年第3期37卷 33-44页

作者： Gebser, Martin Schaub, Torsten Univ Potsdam Potsdam Germany ICLP Doctoral Consortium New York NY USA Inria Rennes Rennes France

answer set programming (ASP) has emerged as an approach to declarative problem solving based on the stable model semantics for logic programs. The basic idea is to represent a computational problem by a logic program, formulating constraints in terms of rules, such that its answer sets correspond to problem solutions. To this end, ASP combines an expressive language for high-level modeling with powerful low-level reasoning capacities, provided by off-the-shelf tools. Compact problem representations take advantage of genuine modeling features of ASP, including (first-order) variables, negation by default, and recursion. In this article, we demonstrate the ASP methodology on two example scenarios, illustrating basic as well as advanced modeling and solving concepts. We also discuss mechanisms to represent and implement extended kinds of preferences and optimization. An overview of further available extensions concludes the article.

关键词： Mathematical optimization Stable model semantics Language Modeling language answer set programming Extension Model

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A Tool for Probabilistic Reasoning Based on Logic programming and First-Order Theories Under Stable Model Semantics 1

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15th European Conference on Logics in Artificial Intelligence (JELIA)

作者： Nickles, Matthias INSIGHT Ctr Data Analyt Galway Ireland Natl Univ Ireland Discipline Informat Technol Galway Ireland

ISBN: (数字)9783319487588

ISBN: (纸本)9783319487588;9783319487571

This System Description paper describes the software framework PrASP ("Probabilistic answer set programming"). PrASP is both an uncertainty reasoning and machine learning software and a probabilistic logic programming language based on answer set programming (ASP). Besides serving as a research software platform for non-monotonic ( inductive) probabilistic logic programming, our framework mainly targets applications in the area of uncertainty stream reasoning. PrASP programs can consist of ASP (AnsProlog) as well as First-Order Logic formulas (with stable model semantics), annotated with conditional or unconditional probabilities or probability intervals. A number of alternative inference algorithms allow to attune the system to different task characteristics (e.g., whether or not independence assumptions can be made).

关键词： Artificial intelligence answer set programming Probabilistic logic programming Statistical-relational learning SAT

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A synthesis of automated planning and reinforcement learning for efficient, robust decision-making

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ARTIFICIAL INTELLIGENCE 2016年第0期241卷 103-130页

作者： Leonetti, Matteo Iocchi, Luca Stone, Peter Univ Texas Austin Dept Comp Sci 2317 SpeedwayStop D9500 Austin TX 78712 USA Sapienza Univ Rome Dept Comp Control & Management Engn Via Ariosto 25 I-00185 Rome Italy Univ Leeds Sch Comp Leeds LS2 9JT W Yorkshire England

Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearniNG (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to, the environment as it changes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Automated planning Reinforcement learning Autonomous robot Robot learning answer set programming

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Anytime answer set optimization via unsatisfiable core shrinking

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THEORY AND PRACTICE OF LOGIC programming 2016年第5-6期16卷 533-551页

作者： Alviano, Mario Dodaro, Carmine Univ Calabria Dept Math & Comp Sci I-87030 Commenda Di Rende Italy

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.

关键词： answer set programming weak constraints unsatisfiable cores

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