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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Gu, Haijun Zhu, Shucun Cui, Yin Miao, Xin Liu, Haoyu Liu, Meiqi JiLin Univ Coll Commun Engn Changchun Peoples R China

ISBN: (纸本)9781728107387

Based on Belief-Desire-Intention(BDI) architecture,the Agent built in answer set programming (ASP) is applied to a security ***, the task planning is described in BDI models for different autonomous agents,which corresponding structure and cooperation mechanism are explained in detail;Secondly, the symbol language L-p including predicate set is created for the security task , which defines the unified abstract symbols for representation, interaction and state *** paper describes the system as a dynamic application program in ASP to solve multi-agents problem. in additional, the method to solve the conflict of beliefs in multi-agent collaboration process is given with a technical framework called Equibel, which shares beliefs among agents and the renewal of their own beliefs. Finally, the theoretical method is simulated and validated with the security application background .The simulation results prove the effectiveness of Agent method, which can upgrade the traditional security system conveniently.

关键词： Agent answer set programming BDI Security Platform

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Dynamic programming on Tree Decompositions with D-FLAT

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KUNSTLICHE INTELLIGENZ 2018年第2-3期32卷 191-192页

作者： Abseher, Michael Bliem, Bernhard Hecher, Markus Moldovan, Marius Woltran, Stefan Univ Helsinki Gustaf Hallstromin Katu 2b Helsinki 00560 Finland TU Wien Favoritenstr 9-11 A-1040 Vienna Austria

Many hard problems can be solved efficiently by dynamic programming algorithms that work on tree decompositions. In this paper, we present the D-FLAT system for rapid prototyping of such algorithms. Users can specify the algorithm for their problem using answer set programming. We illustrate the framework by an example and briefly discuss its main features.

关键词： Tree decomposition answer set programming Dynamic programming

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Constraint-Enhanced Role Engineering via answer set programming 12

Constraint-Enhanced Role Engineering via Answer Set Programm...

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7th ACM Symposium on Information, Computer and Communications Security (ASIACCS)

作者： Hu, Jinwei Khan, Khaled M. Bai, Yun Zhang, Yan Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar Univ Western Sydney Sch Comp & Math Sydney NSW 1797 Australia

ISBN: (纸本)9781450313032

Role engineering (RE) aims to develop and maintain appropriate role-based access control (RBAC) configurations. However, RE with constraints in place is not well-studied. Constraints usually describe organizations' security and business requirements. An inconsistency between configurations and constraints compromises security and availability, as it may authorize otherwise forbidden access and deprive users of due privileges. In this paper, we apply answer set programming (ASP) to discover RBAC configurations that comply with constraints and meet various optimization objectives. We first formulate the need of supporting constraints as a problem independent of and complementary to existing RE problems. We then present a flexible framework for translating the proposed problem to ASP programs. In this way, the problem can be addressed via ASP solvers. Finally, we demonstrate the effectiveness and efficiency of our approach through experimental results.

关键词： RBAC Constraint Role Engineering answer set programming

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Geography Gaokao-Oriented Knowledge Acquisition for Comparative Sentences Based on Logic programming 6th

Geography Gaokao-Oriented Knowledge Acquisition for Comparat...

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6th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Li, Xuelian Liu, Qian Zhu, Man Xu, Feifei Yu, Yunxiu Zhang, Shang Ni, Zhaoxi Gao, Zhiqiang Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Multiple-choice questions of comparing one entity with another in a university's entrance examination like Gaokao in China are very common but require high knowledge skill. As a preliminary attempt to address this problem, we build a geography Gaokao-oriented knowledge acquisition system for comparative sentences based on logic programming to help solve real geography examinations. Our work consists of two consecutive tasks: identify comparative sentences from geographical texts and extract comparative elements from the identified comparative sentences. Specifically, for the former task, logic programming is employed to filter out non-comparative sentences, and for the latter task, the information of dependency grammar and heuristic position is adopted to represent the relations among comparative elements. The experimental results show that our system achieves outstanding performance for practical use.

关键词： Knowledge acquisition Comparative sentences Logic programming answer set programming

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Determining inference semantics for disjunctive logic programs

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ARTIFICIAL INTELLIGENCE 2019年 277卷 103165-000页

作者： Shen, Yi-Dong Eiter, Thomas Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Tech Univ Wien Inst Log & Computat Favoritenstr 9-11 A-1040 Vienna Austria

In a seminal paper, Gelfond and Lifschitz [34] introduced simple disjunctive logic programs, where in rule heads the disjunction operator "vertical bar" is used to express incomplete information, and defined the answer set semantics (called GL-semantics for short) based on a program transformation (called GL-reduct) and the minimal model requirement. Our observations reveal that the requirement of the GL-semantics, i.e., an answer set should be a minimal model of rules of the GL-reduct, may sometimes be too strong a condition and exclude some answer sets that would be reasonably acceptable. To address this, we present an alternative, more permissive answer set semantics, called the determining inference (DI) semantics. Specifically, we introduce a head selection function to formalize the operator vertical bar and define answer sets as follows: (i) Given an interpretation I and a selection function sel, we transform a disjunctive program Pi into a normal program Pi(I)(sel), called a disjunctive program reduct;(ii) given a base answer set semantics chi for normal programs, we define I to be a candidate answer set of Pi w.r.t. chi if I is an answer set of Pi(I)(sel) under chi;and (iii) we define I to be an answer set of Pi w.r.t. chi if I is a minimal candidate answer set. The DI-semantics is general and applicable to extend any answer set semantics chi for normal programs to disjunctive programs. By replacing chi with the GL(nlp)-semantics defined by Gelfond and Lifschitz [33], we induce a DI-semantics for simple disjunctive programs, and by replacing chi with the well justified semantics defined by Shen et al. [65], we further induce a DI-semantics for general disjunctive programs. We also establish a novel characterization of the GL-semantics in terms of a disjunctive program reduct, which reveals the essential difference of the DI-semantics from the GL-semantics and leads us to giving a satisfactory solution to the open problem presented by Hitzler and Seda [36] ab

关键词： answer set programming Knowledge representation and reasoning Nonmonotonic reasoning Disjunctive logic programs

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A computational framework for conceptual blending

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ARTIFICIAL INTELLIGENCE 2018年 256卷 105-129页

作者： Eppe, Manfred Maclean, Ewen Confalonieri, Roberto Kutz, Oliver Schorlemmer, Marco Plaza, Enric Kuehnberger, Kai-Uwe Univ Hamburg Hamburg Germany Int Comp Sci Inst Berkeley CA 94704 USA CSIC IIIA Barcelona Catalonia Spain Univ Edinburgh Edinburgh Midlothian Scotland Free Univ Bozen Bolzano Bolzano Italy Univ Osnabruck Osnabruck Germany

We present a computational framework for conceptual blending, a concept invention method that is advocated in cognitive science as a fundamental and uniquely human engine for creative thinking. Our framework treats a crucial part of the blending process, namely the generalisation of input concepts, as a search problem that is solved by means of modern answer set programming methods to find commonalities among input concepts. We also address the problem of pruning the space of possible blends by introducing metrics that capture most of the so-called optimality principles, described in the cognitive science literature as guidelines to produce meaningful and serendipitous blends. As a proof of concept, we demonstrate how our system invents novel concepts and theories in domains where creativity is crucial, namely mathematics and music. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Computational creativity Conceptual blending Cognitive science answer set programming

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Adjudication of coreference annotations via answer set optimisation

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2018年第4期30卷 525-546页

作者： Schueller, Peter Tech Univ Wien Inst Log & Computat Vienna Austria Marmara Univ Fac Engn Istanbul Turkey

We describe the first automatic approach for merging coreference annotations obtained from multiple annotators into a single gold standard. This merging is subject to certain linguistic hard constraints and optimisation criteria that prefer solutions with minimal divergence from annotators. The representation involves an equivalence relation over a large number of elements. We use answer set programming to describe two representations of the problem and four objective functions suitable for different data-sets. We provide two structurally different real-world benchmark data-sets based on the METU-Sabanci Turkish Treebank and we report our experiences in using the Gringo, Clasp and Wasp tools for computing optimal adjudication results on these data-sets.

关键词： Coreference resolution adjudication answer set programming

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LARS: A Logic-based framework for Analytic Reasoning over Streams

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ARTIFICIAL INTELLIGENCE 2018年 261卷 16-70页

作者： Beck, Harald Minh Dao-Tran Eiter, Thomas Vienna Univ Technol Inst Log & Computat Favoritenstr 9-11 A-1040 Vienna Austria

The increasing availability of streaming data has accelerated advances in information processing tools that no longer store data for static querying but push information to consumers as soon as it becomes available. Stream processing aims at providing languages and tools for data that changes at a high rate. To cope with the volume of data, query languages often extend existing approaches for static data by means of window operators that return snapshots of recent data. However, the semantics of these languages are often given only informally or operationally, which makes their analysis and comparison difficult. A formal means to express the declarative semantics of such systems seems to be missing. This lack of theory is of particular relevance for the emerging research in stream reasoning which shifts the focus from throughput to higher expressiveness. To fill this gap, we present LARS, a Logic-based framework for Analytic Reasoning over Streams. At its core, LARS formulas extend propositional logic with generic window operators and additional controls to handle temporal information. On top of this, LARS programs extend answer set programming (ASP) with rich stream reasoning capabilities;the latter can be exploited to target Al applications in a streaming context, such as diagnosis, configuration or planning. Specifically, we study in this article the computational complexity of LARS formulas and programs, their relationship to Linear Temporal Logic (LTL) and the well-known Continuous Query Language (CQL). Furthermore, we discuss the modeling capabilities of LARS in notes on the SPARQL extensions C-SPARQL and CQELS, and on the interval-based approach of the complex event processing language ETALIS. We finally briefly touch available implementations, in particular, the recent prototype engines Laser and Ticker that aim for high throughput and high expressiveness, respectively. Notably, both engines specify their semantics in LARS, indicating the desired flexibility

关键词： answer set programming Stream reasoning Dynamic data

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Cautious reasoning in ASP via minimal models and unsatisfiable cores

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THEORY AND PRACTICE OF LOGIC programming 2018年第3-4期18卷 319-336页

作者： Alviano, Mario Dodaro, Carmine Jarvisalo, Matti Maratea, Marco Previti, Alessandro Univ Calabria DEMACS Commenda Di Rende Italy Univ Genoa DIBRIS Genoa Italy Univ Helsinki Dept Comp Sci HIIT Helsinki Finland

answer set programming (ASP) is a logic-based knowledge representation framework, supporting-among other reasoning modes-the central task of query answering. In the propositional case, query answering amounts to computing cautious consequences of the input program among the atoms in a given set of candidates, where a cautious consequence is an atom belonging to all stable models. Currently, the most efficient algorithms either iteratively verify the existence of a stable model of the input program extended with the complement of one candidate, where the candidate is heuristically selected, or introduce a clause enforcing the falsity of at least one candidate, so that the solver is free to choose which candidate to falsify at any time during the computation of a stable model. This paper introduces new algorithms for the computation of cautious consequences, with the aim of driving the solver to search for stable models discarding more candidates. Specifically, one of such algorithms enforces minimality on the set of true candidates, where different notions of minimality can be used, and another takes advantage of unsatisfiable cores computation. The algorithms are implemented in WASP, and experiments on benchmarks from the latest ASP competitions show that the new algorithms perform better than the state of the art.

关键词： answer set programming Cautious Reasoning Query answering

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ASP Based Generation of Information Terms for Constructive εL

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FUNDAMENTA INFORMATICAE 2018年第1-2期161卷 29-51页

作者： Bozzato, Loris Fdn Bruno Kessler Via Sommar 18 I-38123 Trento Italy

Constructive description logics define interpretations of description logics under different constructive semantics. These logics have been mostly studied from the point of view of their formal properties: limited practical approaches have been shown for their use in knowledge representation and Semantic Web languages and tools (which, on the other hand, constitute the distinctive applications of description logics). In this paper we demonstrate a solution to address this aspect: from the theoretical point of view, we first introduce an information terms semantics for the minimal description logic epsilon L and we establish formal results linking this constructive semantics to answer set semantics. Using these results, on the practical side, we then present a prototype managing one aspect of such semantics (the generation of information terms of a knowledge base) using OWL-EL ontologies and "off the shelf" tools.

关键词： Constructive description logics Information terms semantics answer set programming

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