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Exploiting Answer Set programming with External Sources for Meta-Interpretive Learning

THEORY AND PRACTICE OF logic programming 2018年第3-4期18卷 571-588页

作者： Kaminski, Tobias Eiter, Thomas Inoue, Katsumi Tech Univ Vienna TU Wien Vienna Austria Natl Inst Informat Tokyo Japan

Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system based on Prolog. Viewing MIL-problems as combinatorial search problems, they can alternatively be solved by employing Answer Set programming (ASP), which may result in performance gains as a result of efficient conflict propagation. However, a straightforward ASP-encoding of MIL results in a huge search space due to a lack of procedural bias and the need for grounding. To address these challenging issues, we encode MIL in the HEX-formalism, which is an extension of ASP that allows us to outsource the background knowledge, and we restrict the search space to compensate for a procedural bias in ASP. This way, the import of constants from the background knowledge can for a given type of meta-rules be limited to relevant ones. Moreover, by abstracting from term manipulations in the encoding and by exploiting the HEX interface mechanism, the import of such constants can be entirely avoided in order to mitigate the grounding bottleneck. An experimental evaluation shows promising results.

关键词： inductive logic programming Meta-Interpretive Learning Answer Set programming

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Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

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THEORY AND PRACTICE OF logic programming 2018年第3-4期18卷 623-637页

作者： Mitra, Arindam Baral, Chitta Arizona State Univ Tempe AZ 85287 USA

Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the inductive logic programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), whicYh to the best of our knowledge was not previously possible. The system is publicly available at https://***/KdWAcV.

关键词： inductive logic programming Answer Set programming Question Answering Handwritten Digit Recognition Context Dependent Learning

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Evaluation of inductive logic programming for information extraction from natural language texts to support spatial data recommendation services

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2011年第11期25卷 1809-1827页

作者： Smole, Domen Ceh, Marjan Podobnikar, Tomaz DFG CONSULTING Ltd Ljubljana Slovenia Univ Ljubljana Dept Geodet Engn Fac Civil & Geodet Engn Ljubljana Slovenia Slovenian Acad Sci & Arts Inst Anthropol & Spatial Studies Ctr Sci Res Ljubljana Slovenia

In this article we analyze a well-known and extensively researched problem: how to find all datasets, on the one hand, and on the other hand only those that are of value to the user when dealing with a specific spatially oriented task. In analogy with existing approaches to a similar problem from other fields of human endeavor, we call this software solution 'a spatial data recommendation service.' In its final version, this service should be capable of matching requests created in the user's mind with the content of the existing datasets, while taking into account the user's preferences obtained from the user's previous use of the service. As a result, the service should recommend a list of datasets best suited to the user's needs. In this regard, we consider metadata, particularly natural language definitions of spatial entities, a crucial piece of the solution. To be able to use this information in the process of matching the user's request with the dataset content, this information must be semantically preprocessed. To automate this task we have applied a machine learning approach. With inductive logic programming (ILP) our system learns rules that identify and extract values for the five most frequent relations/properties found in Slovene natural language definitions of spatial entities. The initially established quality criterion for identifying and extracting information was met in three out of five examples. Therefore we conclude that ILP offers a promising approach to developing an information extraction component of a spatial data recommendation service.

关键词： metadata spatial entities semantics inductive logic programming

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An Instance Based Model for Scalable θ-Subsumption 29

An Instance Based Model for Scalable θ-Subsumption

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29th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Leger, Hippolyte Bouthinon, Dominique Lebbah, Mustapha Azzag, Hanene Univ Paris 13 LIPN UMR CNRS 7030 99 Ave Jean Baptiste Clement F-93430 Villetaneuse France

ISBN: (纸本)9781538638767

The theta-subsumption test is known to be a bottleneck in inductive logic programming. The state-of-the-art learning systems in this field are hardly scalable. Last year, we have created a distributed theta-subsumption process based on an Actor Model, with the aim of being able to decide subsumption on very large clauses. This model was correct and complete, but was also very slow. This is why we introduce ANTS (Actor Network based Theta-Subsumption), a new model also based on an actor network, which is significantly faster than the previous one.

关键词： theta-subsumption inductive logic programming actor model distributed computing Akka

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Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Incremental and Iterative Learning of Answer Set Programs fr...

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34th International Conference on logic programming (ICLP)

作者： Mitra, Arindam Baral, Chitta Arizona State Univ Tempe AZ 85287 USA

关键词： inductive logic programming Answer Set programming Question Answering Handwritten Digit Recognition Context Dependent Learning

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Optimal PMU Placement using ILP and ACO:A Comparative Study

Optimal PMU Placement using ILP and ACO:A Comparative Study

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Global Conference for Advancement in Technology (GCAT)

作者： Nikita U. Nimbalkar Dr.Prasad M. Joshi Department of Electrical Engineering Dnyanshree Institute of Engineering & Technology Satara India Department of Electrical Engineering Government College of Engineering Karad India

ISBN: (纸本)9781728136950

Phasor Measurement Unit (PMU) plays a key role for control and safety of a power system. For developing a smart grid, optimal placement of PMUs is necessary. Mathematical, Heuristic and Metaheuristic methods are widely used for solving Optimal PMU Placement (OPP). In this paper, a comparison is made between Mathematical method Integer Linear programming (ILP) and Metaheuristic method Ant Colony Optimization (ACO). The comparison of results of both the methods for IEEE 14 Bus system were obtained in MATLAB and it is seen that ILP is more useful than ACO.

关键词： ant colony optimisation integer programming linear programming phasor measurement phasor measurement Phasor measurement units inductive logic programming Integer programming linear programming Ant colony optimization ADMINISTRATIVE CONSENT ORDER placement power systems Smart grids

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LearnSec: A Framework for Full Text Analysis 13th

LearnSec: A Framework for Full Text Analysis

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13th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Goncalves, Carlos Iglesias, E. L. Borrajo, L. Camacho, Rui Seara Vieira, A. Goncalves, Celia Talma Univ Vigo Higher Tech Sch Comp Engn Campus Univ Lagoas S-N Orense 32004 Spain FEUP U Porto Rua Dr Roberto Frias S-N P-4200465 Porto Portugal LIAAD INESC TEC Porto Portugal ISCAP P Porto Rua Jaime Lopes Amorim S-N P-4465004 Sao Mamede de Infesta Portugal U Porto LIACC Porto Portugal

ISBN: (纸本)9783319926391;9783319926384

Large corpus of scientific research papers have been available for a long time. However, most of those corpus store only the title and the abstract of the paper. For some domains this information may not be enough to achieve high performance in text mining tasks. This problem has been recently reduced by the growing availability of full text scientific research papers. A full text version provides more detailed information but, on the other hand, a large amount of data needs to be processed. A priori, it is difficult to know if the extra work of the full text analysis has a significant impact in the performance of text mining tasks, or if the effect depends on the scientific domain or the specific corpus under analysis. The goal of this paper is to show a framework for full text analysis, called LearnSec, which incorporates domain specific knowledge and information about the content of the document sections to improve the classification process with propositional and relational learning. To demonstrate the usefulness of the tool, we process a scientific corpus based on OSHUMED, generating an attribute/value dataset in Weka format and a First Order logic dataset in inductive logic programming (ILP) format. Results show a successful assessment of the framework.

关键词： Full text analyses Text preprocessing Text mining Use of background knowledge inductive logic programming

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Towards Human Activity Reasoning with Computational logic and Deep Learning 18

Towards Human Activity Reasoning with Computational Logic an...

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10th Hellenic Conference on Artificial Intelligence (SETN)

作者： Prapas, Ioannis Paliouras, Georgios Artikis, Alexander Baskiotis, Nicolas NCSR Demokritos Athens Greece Natl Tech Univ Athens Athens Greece Univ Piraeus Piraeus Greece Univ Paris 06 Paris France

ISBN: (纸本)9781450364331

We approach the problem of human action recognition in videos by distinguishing between simple and complex actions. To recognize simple actions, we take advantage of the latest advances with 3D convolutional networks, which are able to offer a generic video snippet descriptor. For the complex ones, which involve interaction between more than one individual, we use the recognized simple human actions of the previous step to generate Event Calculus theories. This way, we aim to achieve a high-level human action understanding, combining the opaque effectiveness of deep learning and the transparent reasoning of computational logic. Our experimental results on a benchmark activity recognition dataset encourage further research towards this direction.

关键词： Activity Recognition Activity Reasoning C3D features Event Calculus inductive logic programming

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Transformation Learning in the Context of Model-Driven Data Warehouse: An Experimental Design Based on inductive logic programming

Transformation Learning in the Context of Model-Driven Data ...

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23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Essaidi, Moez Osmani, Aomar Rouveirol, Celine Univ Paris 13 LIPN UMR CNRS 7030 F-93430 Villetaneuse France

ISBN: (纸本)9780769545967

Model transformation in the context of Model-Driven Data Warehouse is ensured by human experts. It generates an exorbitant cost and requires high proficiency. We propose in this paper a machine learning approach to reduce the expert contribution in the transformation process. We propose to express the model transformation problem as an inductive logic programming one and to use existing project traces to find the best business transformation rules. We used the Aleph ILP system to learn such rules. Obtained results show that found rules are close to expert ones. Within our application context, we need to deal with several dependent concepts. Taking into account work in Layered Learning, we propose a new methodology that automatically updates the background knowledge of the concepts to be learned. Experimental results support the conclusion that this approach is suitable to solve this kind of problem.

关键词： Model-Driven Data Warehouse inductive logic programming Dependent-Concept Learning

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inductive Learning from State Transitions over Continuous Domains 1

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27th International Conference on inductive logic programming (ILP)

作者： Ribeiro, Tony Tourret, Sophie Folschette, Maxime Magnin, Morgan Borzacchiello, Domenico Chinesta, Francisco Roux, Olivier Inoue, Katsumi Lab Sci Numer Nantes LS2N 1 Rue Noe F-44321 Nantes France Max Planck Inst Informat Saarland Informat Campus D-66123 Saarbrucken Germany Univ Rennes CNRS Inria IRISAIRSET F-35000 Rennes France Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Inst Calcul Intensif 1 Rue Noe F-44321 Nantes France ENSAM ParisTech PIMM 151 Blvd Hop F-75013 Paris France

ISBN: (数字)9783319780900

ISBN: (纸本)9783319780900;9783319780894

Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to discrete variables or suppose a discretization of continuous data. However, when working with real data, the discretization choices are critical for the quality of the model learned by LFIT. In this paper, we focus on a method that learns the dynamics of the system directly from continuous time-series data. For this purpose, we propose a modeling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values.

关键词： Continuous logic programming Learning from interpretation transition Dynamical systems inductive logic programming

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