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25th international conference on inductive logic programming, ilp 2015

25th international conference on inductive logic programming, ilp 2015

ISBN: (纸本)9783319405650

the proceedings contain 14 papers. the special focus in this conference is on inductive logic programming. the topics include: Relational kernel-based grasping with numerical features;complex aggregates within random forests;distributed parameter learning for probabilistic ontologies;meta-interpretive learning of data transformation programs;statistical relational learning with soft quantifiers;ontology learning from interpretations in lightweight description logics;constructing markov logic networks from first-order default rules;a note on mining all graphs;processing markov logic networks with GPUs;using ilp to identify pathway activation patterns in systems biology;an algebraic prolog for kernel programming;an exercise in declarative modeling for relational query mining;learning inference by induction and identification of transition models of biological systems in the presence of transition noise.

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Relational data factorization

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MACHINE LEARNING 2017年第12期106卷 1867-1904页

作者： Paramonov, Sergey van Leeuwen, Matthijs De Raedt, Luc Katholieke Univ Leuven Machine Learning Dept Comp Sci Leuven Belgium Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In relational data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. thus, relational data factorization is a relational analog of matrix factorization;it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. the result of relational data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original data). Relational data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving relational data factorization problems.

关键词： Answer set programming inductive logic programming Pattern mining Relational data Factorization Data mining Declarative modeling

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Planning in hybrid relational MDPs

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MACHINE LEARNING 2017年第12期106卷 1905-1932页

作者： Nitti, Davide Belle, Vaishak De Laet, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci B-3001 Leuven Belgium Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland Katholieke Univ Leuven Fac Engn Sci B-3001 Leuven Belgium

We study planning in relational Markov decision processes involving discrete and continuous states and actions, and an unknown number of objects. this combination of hybrid relational domains has so far not received a lot of attention. While both relational and hybrid approaches have been studied separately, planning in such domains is still challenging and often requires restrictive assumptions and approximations. We propose HYPE: a sample-based planner for hybrid relational domains that combines model-based approaches with state abstraction. HYPE samples episodes and uses the previous episodes as well as the model to approximate the Q-function. In addition, abstraction is performed for each sampled episode, this removes the complexity of symbolic approaches for hybrid relational domains. In our empirical evaluations, we show that HYPE is a general and widely applicable planner in domains ranging from strictly discrete to strictly continuous to hybrid ones, handles intricacies such as unknown objects and relational models. Moreover, empirical results showed that abstraction provides significant improvements.

关键词： MDP Probabilistic planning logic programming Relational MDP Hybrid Hybrid relational MDP Probabilistic programming Abstraction logical regression Importance sampling

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Constructing Markov logic Networks from First-Order Default Rules 1

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25th international conference on inductive logic programming (ilp)

作者： Kuzelka, Ondrej Davis, Jesse Schockaert, Steven Cardiff Univ Sch Comp Sci & Informat Cardiff S Glam Wales Katholieke Univ Leuven Dept Comp Sci Leuven Belgium

ISBN: (数字)9783319405667

ISBN: (纸本)9783319405667;9783319405650

Expert knowledge can often be represented using default rules of the form "if A then typically B". In a probabilistic framework, such default rules can be seen as constraints on what should be derivable by MAP-inference. We exploit this idea for constructing a Markov logic network M from a set of first-order default rules D, such that MAP inference from M exactly corresponds to default reasoning from D, where we view first-order default rules as templates for the construction of propositional default rules. In particular, to construct appropriate Markov logic networks, we lift three standard methods for default reasoning. the resulting Markov logic networks could then be refined based on available training data. Our method thus offers a convenient way of using expert knowledge for constraining or guiding the process of learning Markov logic networks.

关键词： Markov processes

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Processing Markov logic Networks with GPUs: Accelerating Network Grounding 1

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25th international conference on inductive logic programming (ilp)

作者： Alberto Martinez-Angeles, Carlos Dutra, Ines Costa, Vitor Santos Buenabad-Chavez, Jorge CINVESTAV IPN Dept Computac Av Inst Politecn Nacl 2508 Mexico City 07360 DF Mexico CRACS INESC TEC LA Dept Ciencia Comp Rua Campo Alegre 1021 P-4169007 Oporto Portugal Univ Porto Rua Campo Alegre 1021 P-4169007 Oporto Portugal

ISBN: (数字)9783319405667

ISBN: (纸本)9783319405667;9783319405650

Markov logic is an expressive and widely used knowledge representation formalism that combines logic and probabilities, providing a powerful framework for inference and learning tasks. Most Markov logic implementations perform inference by transforming the logic representation into a set of weighted propositional formulae that encode a Markov network, the ground Markov network. Probabilistic inference is then performed over the grounded network. Constructing, simplifying, and evaluating the network are the main steps of the inference phase. As the size of a Markov network can grow rather quickly, Markov logic Network (MLN) inference can become very expensive, motivating a rich vein of research on the optimization of MLN performance. We claim that parallelism can have a large role on this task. Namely, we demonstrate that widely available Graphics Processing Units (GPUs) can be used to improve the performance of a state-of-the-art MLN system, Tuffy, with minimal changes. Indeed, comparing the performance of our GPU-based system, TuGPU, to that of the Alchemy, Tuffy and RockIt systems on three widely used applications shows that TuGPU is up to 15x times faster than the other systems.

关键词： Statistical relational learning Markov logic Markov logic networks Datalog Parallel computing GPUs

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Case studies on extracting the characteristics of the reachable states of state machines formalizing communication protocols with inductive logic programing 25

Case studies on extracting the characteristics of the reacha...

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25th international conference on inductive logic programming, lbp-ilp 2015

作者： Ho, Dung Tuan Zhang, Min Ogata, Kazuhiro Japan China

A distributed system DS can be formalized as a state machine M and many desired properties of DS can be expressed as invariants of M. An invariant of M is a state predicate p of M such that p holds for all reachable states of M. To verify that DS enjoys a desired property, namely to prove that p is an invariant of M, we often need to find other invariants as lemmas, which is one of the most intellectual activities in Interactive theorem Proving (ITP). For this end, our experiences on ITP tell us that it is useful to get better understandings of the reachable states RM of M. We report on case studies in which Progol, an inductive logic programming (ilp) system, has been used to extract the characteristics of the reachable states of state machines formalizing communication protocols. the case studies demonstrate that ilp has potential abilities to extract the characteristics of RM. © 2015, CEUR-WS. All rights reserved.

关键词： Computer circuits

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2015 25th international conference on Information, Communication and Automation Technologies, ICAT 2015 - Proceedings

2015 25th International Conference on Information, Communica...

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25th international conference on Information, Communication and Automation Technologies, ICAT 2015

ISBN: (纸本)9781467381468

the proceedings contain 47 papers. the topics discussed include: hardware-in-the-loop simulations and control design for a small vertical axis wind turbine;learning from depth sensor data using inductive logic programming;design and application of an active suspension system on a 6 DOF half vehicle model;time domain disturbance observer based control of a quadrotor unmanned aerial vehicle;sampled data output regulation of a linear system based on finite time impulsive observer;a haptic-assisted guidance system for working machines based on virtual force fields;classification of interruption causes used in quality regulation - case of BiH;inverse feedforward control with output polynomial smoothing;and implementation of enterprise GIS in power distribution utilities.

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25th international conference on Automated Deduction CADE 2015

25th International Conference on Automated Deduction CADE 20...

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25th international conference on Automated Deduction CADE 2015

the proceedings contain 42 papers. the special focus in this conference is on Automated Deduction. the topics include: History and prospects for first-order automated deduction;stumbling around in the dark: lessons from everyday mathematics;automated reasoning in the wild;confluence competition 2015;term rewriting with prefix context constraints and bottom-up strategies;encoding dependency pair techniques and control strategies for maximal completion;reducing relative termination to dependency pair problems;decidability of univariate real algebra with predicates for rational and integer powers;a formalisation of finite automata using hereditarily finite sets;inductive beluga: programming proofs;quantifier-free equational logic and prime implicate generation;quantomatic: a proof assistant for diagrammatic reasoning;cooperating proof attempts;towards the compression of first-order resolution proofs by lowering unit clauses;a polite non-disjoint combination method: theories with bridging functions revisited;exploring theories with a model-finding assistant;abstract interpretation as automated deduction;a uniform substitution calculus for differential dynamic logic;program synthesis using dual interpretation;automated theorem proving for assertions in separation logic with all connectives;tableaux methods for propositional dynamic logics with separating parallel composition;regular patterns in second-order unification;theorem proving with bounded rigid e-unification and linear integer arithmetic revisited.

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Function-Level Processor (FLP): Raising Efficiency by Operating at Function Granularity for Market-Oriented MPSoC

Function-Level Processor (FLP): Raising Efficiency by Operat...

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IEEE 25th international conference on Application-Specific Systems, Architectures and Processors (ASAP)

作者： Tabkhi, Hamed Bushey, Robert Schirner, Gunar Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Analog Devices Inc ADI Embedded Syst Prod & Technol Norwood MA USA

ISBN: (纸本)9781479936090

the exponential growth in computation demand drives chip vendors to heterogeneous architectures combining Instruction-Level Processors (ilps) and custom HW Accelerators (HWACCs) in an attempt to provide the needed processing capabilities while meeting power/energy requirements. ilps, on one hand, are highly flexible, but power inefficient. Custom HWACCs, on the other hand, are inflexible (focusing on dedicated kernels), but highly power efficient. Since, designing HWACCs for every application is cost prohibitive, large portions of applications still run inefficiently on ilps. New processing architectures are needed that combine the power efficiency of HWACCs while still retaining sufficient flexibility to realize applications across targeted market segments. this paper introduces Function-Level Processors (FLPs) to fill the gap between ilps and dedicated HWACCs. FLPs are comprised of configurable Function Blocks (FBs) implementing selected functions which are then interconnected via programmable point-to-point connections constructing an extensible/configurable macro data-path. An FLP raises programming abstraction to a Function-Set Architecture (FSA) controlling FBs allocation, configuration and scheduling. We demonstrate FLP benefits with an industry example of the Pipeline-Vision Processor (PVP). We highlight the gained flexibility by mapping 10 embedded vision applications entirely to the FLP-PVP offering up to 22.4 GOPs/s with average power of 120 mW. the results also demonstrate that our FLP-PVP solution consumes 14x-18x less power than an ilp and 5x less power than a hybrid ilp+ HWACCs solution.

关键词： fault-location problem PROCESSOR inductive logic programming dynamic efficiency Exponential Growth average power ARCHITECTURE Power (Psychology) Energy sources macro data Target marketing Connection points

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