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Semantics-Based Multidimensional Query Over Sparse Data Marts 17th

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17th international conference on Big Data Analytics and Knowledge Discovery (DaWaK)

作者： Diamantini, Claudia Potena, Domenico Storti, Emanuele Univ Politecn Marche Dipartimento Ingn Informaz I-60131 Ancona Italy

ISBN: (纸本)9783319227290;9783319227283

Measurement of Performances Indicators (PIs) in highly distributed environments, especially in networked organisations, is particularly critical because of heterogeneity issues and sparsity of data. In this paper we present a semantics-based approach for dynamic calculation of PIs in the context of sparse distributed data marts. In particular, we propose to enrich the multidimensional model with the formal description of the structure of an indicator given in terms of its algebraic formula and aggregation function. Upon such a model, a set of reasoning-based functionalities are capable to mathematically manipulate formulas for dynamic aggregation of data and computation of indicators on-the-fly, by means of recursive application of rewriting rules based on logic programming.

关键词： logic-based formalisation of performance indicators Multidimensional model Sparse data marts Query rewriting Virtual enterprises

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Optimising Business Process Discovery Using Answer Set programming 16th

Optimising Business Process Discovery Using Answer Set Progr...

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16th international conference on logic programming and non-monotonic reasoning (LPNMR)

作者： Chesani, Federico Di Francescomarino, Chiara Ghidini, Chiara Grundler, Giulia Loreti, Daniela Maggi, Fabrizio Maria Mello, Paola Montali, Marco Tessaris, Sergio DISI Univ Bologna Bologna Italy Fdn Bruno Kessler Trento Italy Free Univ Bozen Bolzano Bolzano Italy

ISBN: (纸本)9783031157073;9783031157066

Declarative business process discovery aims at identifying sets of constraints, from a given formal language, that characterise a workflow by using pre-recorded activity logs. Since the provided logs represent a fraction of all the consistent evolution of a process, and the fact that many sets of constraints covering those examples can be selected, empirical criteria should be employed to identify the "best" candidates. In our work we frame the process discovery as an optimisation problem, where we want to identify optimal sets of constraints according to preference criteria. Declarative constraints for processes are usually characterised via temporal logics, so different solutions can be semantically equivalent. For this reason, it is difficult to use an arbitrary finite domain constraints solvers for the optimisation. the use of Answer Set programming enables the combination of deduction rules within the optimisation algorithm, in order to take into account not only the user preferences but also the implicit semantics of the formal language. In this paper we show how we encoded the process discovery problem using the ASPrin framework for qualitative and quantitative optimisation in ASP, and the results of our experiments.

关键词： Preferences Answer set programming Optimisation Process mining Process discovery Declarative process models

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FreeEnCal: A forward reasoning engine with general-purpose

FreeEnCal: A forward reasoning engine with general-purpose

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11th international conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks

作者： Cheng, Jingde Nara, Shinsuke Goto, Yuichi Saitama Univ Dept Informat & Comp Sci Saitama 3388570 Japan

ISBN: (纸本)9783540748267

A forward reasoning engine is an indispensable component in many advanced knowledge-based systems with purposes of creation, discovery, or prediction. this paper presents a forward reasoning engine with general-purpose, named "FreeEnCal", which can interpret and perform inference rules defined and given by its users, draw fragments of various classical and/or non-classical logic systems formalized as different formal systems, draw empirical theorems of various formal theories constructed based on various logic systems, and perform deductive, inductive, and abductive reasoning automatically. FreeEnCal can be used as a ready-made forward reasoning engine serving as a core and fundamental component in various advanced knowledge-based systems as well as an alone forward reasoning engine with general-purpose. the paper presents our basic ideas to design and implement FreeEnCal, facilities provided by FreeEnCal, and some applications of FreeEnCal.

关键词： creation discovery prediction deduction induction abduction advanced knowledge-based systems

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the Automatic Integration of Folksonomies with Taxonomies Using non-axiomatic logic

The Automatic Integration of Folksonomies with Taxonomies Us...

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17th international conference on Information Systems Development

作者： Geldart, Joe Cummins, Stephen Univ Durham Dept Comp Sci Durham England

ISBN: (纸本)9780387848099

Cooperative tagging systems such as folksonomies are powerful tools when used to annotate information resources. the inherent power of folksonomies is in their ability to allow casual users to easily contribute ad hoc, yet meaningful, resource metadata without any specialist training. Older folksonomies have begun to degrade due to the lack of internal structure and from the use of many low quality tags. this chapter describes a remedy for some of the problems associated with folksonomies. We introduce a method of automatic integration and inference of the relationships between tags and resources in a folksonomy using non-axiomatic logic. We test this method on the CiteULike corpus of tags by comparing precision and recall between it and standard keyword search. Our results show that non-axiomatic reasoning is a promising technique for integrating tagging systems with more structured knowledge representations.

关键词： Folksonomy Ontology Intelligent information systems

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Learning to Rank the Distinctiveness of Behaviour in Serial Offending 16th

Learning to Rank the Distinctiveness of Behaviour in Serial ...

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16th international conference on logic programming and non-monotonic reasoning (LPNMR)

作者： Law, Mark Sautory, theophile Mitchener, Ludovico Davies, Kari Tonkin, Matthew Woodhams, Jessica Alrajeh, Dalal ILASP Ltd Grantham England Univ Calif Berkeley Berkeley CA 94720 USA Imperial Coll London London England Univ Bournemouth Bournemouth Dorset England Univ Leicester Leicester Leics England Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9783031157073;9783031157066

Comparative Case Analysis is an analytical process used to detect serial offending. It focuses on identifying distinctive behaviour that an offender displays consistently when committing their crimes. In practice, crime analysts consider the context in which each behaviour occurs to determine its distinctiveness, which subsequently impacts on their determination of whether crimes are committed by the same person or not. Existing algorithms do not currently consider context in this way when generating linkage predictions. this paper presents the first learning-based approach aimed at identifying contexts within which behaviour may be considered more distinctive. We show how this problem can be modelled as that of learning preferences (in answer set programming) from examples of ordered pairs of contexts in which a behaviour was observed. In this setting, a context is preferred to another context if the behaviour is rarer in the first context. We make novel use of odds ratios to determine which examples are used for learning. Our approach has been applied to a real dataset of serious sexual offences provided by the UK National Crime Agency. the approach provides (i) a systematic methodology for selecting examples from which to learn preferences;(ii) novel insights for practitioners into the contexts under which an exhibited behaviour is more rare.

关键词： Crime Linkage Answer Set programming Inductive logic programming

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GPU-Based Parallelism for ASP-Solving 22nd

GPU-Based Parallelism for ASP-Solving

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22nd international conference on Applications of Declarative programming and Knowledge Management, INAP 2019, the 33rd Workshop on logic programming, WLP 2019, and the 27th Workshop on Functional and Constraint logic programming, WFLP 2019

作者： Dovier, Agostino Formisano, Andrea Vella, Flavio Dipartimento di Scienze Matematiche Informatiche e Fisiche Università di Udine Udine Italy Dipartimento di Matematica e Informatica Università di Perugia Perugia Italy Facoltà di Scienze e Tecnologie Informatiche Libera Università di Bolzano Bolzano Italy

ISBN: (纸本)9783030467135

Answer Set programming (ASP) has become the paradigm of choice in the field of logic programming and non-monotonic reasoning. With the design of new and efficient solvers, ASP has been successfully adopted in a wide range of application domains. Recently, with the advent of GPU Computing, which allowed the use of modern parallel Graphical Processing Units (GPUs) for general-purpose computing, new opportunities for accelerating ASP computation has arisen. In this paper, we describe a new approach for solving ASP that exploits the parallelism provided by GPUs. the design of a GPU-based solver poses various challenges due to the peculiarities of GPU in terms of both programmability and architecture capabilities with respect to the intrinsic nature of the satisfiability problems, which exposes poor parallelism. © 2020, Springer Nature Switzerland AG.

关键词： logic programming

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Workshops on Commonsense reasoning, Intelligent Agents, and Distributed Artificial Intelligence, AI 1997

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10th Australian Joint conference on Artificial Intelligence, AI 1997, 2nd Australian Workshop Commonsense reasoning, 2nd Workshop on theoretical and Practical Foundations of Intelligent Agents and 3rd Australian Workshop on Distributed Artificial Intelligence, 1997

ISBN: (纸本)3540647694

the proceedings contain 17 papers. the special focus in this conference is on Formal Models of Agency and reasoning Agents. the topics include: Specifying communicative multi-agent systems;making it up as they go along;agency and the logic of ability;on the relation between interpreted systems and kripke models;disjunctive logic programming and possible model semantics;a non-monotonic ATMS based on annotated logic programs with strong negation;switching between reasoning and search;the social dimension of interactions in multiagent systems;generating states of joint commitment between autonomous agents;social co-ordination among autonomous problem-solving agents;an algorithm for plan verification in multiple agent systems;a framework for coordination and learning among teams of agents;an iterated hawk-and-dove game;a game-theoretic solution of conflicts among competitive agents;transformation between the EMYCIN model and the bayesian network;using multi-agent approach for the design of an intelligent learning environment and a case-based strategy for solution synthesis among cooperative expert systems.

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A Belief Evolution Model with non-axiomatic logic 1

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17th international conference on Knowledge Science, Engineering and Management (KSEM)

作者： Slam, Nady Feng, Bojie Northwest Minzu Univ Key Lab Linguist & Cultura Comp Minist Educ Lanzhou Peoples R China Univ Coll Dublin Informat Syst Dublin Ireland

ISBN: (数字)9789819754984

ISBN: (纸本)9789819754977;9789819754984

the existing belief models based on knowledge science and engineering have not yet formed a complete theoretical system in terms of formation of knowledge, the representation of uncertainty, and the updating and revision of beliefs. To address this problem, this paper takes knowledge ecology as a main reference frame and proposes a belief evolution model using non-Axiomatic logic as a formalization way. the proposed belief systems organize perceived environmental information into evidence, and transform it into normative knowledge, commonsense knowledge, and intelligent strategies through a unified reasoning and learning approach. In addition, the model uniformly characterizes the randomness, vagueness, imprecision, ignorance and other kinds of uncertainty information existing in the knowledge with NAL, which provides a formal representation and semantic explanation of the shape of the knowledge at each stage. the proposed model not only reveals the developmental laws of the internal growth of knowledge, but also explicitly characterizes the transformation and developmental laws between information, knowledge and intelligence, reflecting the constructivist cognitive theory of knowledge and understanding established by Jean Piaget.

关键词： Knowledge representation and reasoning Ecology of Knowledge non-Axiomatic logic Uncertainty Representations Belief revision Constructivist theory of Cognition

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Psychiatric diagnosis from the viewpoint of computational logic 1

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1st international conference on Computational logic, CL 2000

作者： Gartner, Joseph Swift, Terrance Tien, Allen Damásio, Carlos Viegas Pereira, Luís Moniz Medicine Rules Inc. 25 East Loop Rd. Stony Broon NY 11794 United States Department of Computer Science SUNY at Stony Brook Stony Brook NY United States Medical Decision Logic Inc. 7921 Ruxway Rd. Baltimore MD 21204-3515 United States A.I. Centre Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa 2825-114 Caparica Portugal

ISBN: (数字)9783540449577

ISBN: (纸本)3540677976

While medical information systems have become common in the United States, commercial systems that automate or assist in the process of medical diagnosis remain uncommon. this is not surprising, since automating diagnosis requires considerable sophistication both in the understanding of medical epidemeology and in knowledge representation techniques. this paper is an interdisciplinary study of how recent results in logic programming and non-monotonic reasoning can aid in psychiatric diagnosis. We argue that to logically represent psychiatric diagnosis as codified in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition requires abduction over programs that include both explicit and non-stratified default negation, as well as dynamic rules that express preferences between conclusions. We show how such programs can be translated into abductive frameworks over normal logic programs and implemented using recently introduced logic programming techniques. Finally, we note how such programs are used in a commercial product Diagnostica. © Springer-Verlag Berlin Heidelberg 2000.

关键词： logic programming

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Deep learning for probabilistic logic programming 17

Deep learning for probabilistic logic programming

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Doctoral Consortium (DC) of the 17th international conference of the Italian Association for Artificial Intelligence, AI*IA 2018 DC

作者： Fadja, Arnaud Nguembang Riguzzi, Fabrizio Lamma, Evelina Dipartimento di Ingegneria University of Ferrara Italy Dipartimento di Matematica e Informatica University of Ferrara Via Saragat 1 FerraraI-44122 Italy

Due to its expressiveness and intuitiveness, Probabilistic logic programming (PLP) is a useful tool for reasoning in relational domains with uncertainty. However, both inference and learning are expensive tasks. In this paper we present various approaches for speeding up learning. We first consider a restriction of PLP called Liftable PLP (LPLP) in which clauses in the program share the same predicate (the target). then we extend this restriction in Hierarchical PLP (HPLP) where predicates and clauses are hierarchically organized and can be translated into Deep Neural Networks or Arithmetic Circuits. For LPLP, we propose two parameter learning algorithms, Expectation Maximization (EM) and Limited memory BFGS (LBFGS), and a discriminative structure learning. We also propose and implement an algorithm, called Parameter learning for HIerarchical probabilistic logic program (PHIL)3 that learns the parameter of HPLP using EM and gradient method. © 2018 CEUR-WS. All Rights Reserved.

关键词： Gradient methods

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