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Characterizing Predicate Arity and Spatial Structure for inductive Learning of Game Rules 13

13th European Conference on Computer Vision (ECCV)

作者： Dwibedi, Debidatta Mukerjee, Amitabha Indian Inst Technol Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9783319161815;9783319161808

Where do the predicates in a game ontology come from? We use RGBD vision to learn a) the spatial structure of a board, and b) the number of parameters in a move or transition. These are used to define state-transition predicates for a logical description of each game state. Given a set of videos for a game, we use an improved 3D multi-object tracking to obtain the positions of each piece in games such as 4-peg solitaire or Towers of Hanoi. The spatial positions occupied by pieces over the entire game is clustered, revealing the structure of the board. Each frame is represented as a Semantic Graph with edges encoding spatial relations between pieces. Changes in the graphs between game states reveal the structure of a "move". Knowledge from spatial structure and semantic graphs is mapped to FOL descriptions of the moves and used in an inductive logic framework to infer the valid moves and other rules of the game. Discovered predicate structures and induced rules are demonstrated for several games with varying board layouts and move structures.

关键词： Predicate discovery Spatial structure discovery Game rule learning Semantic graphs Multi-object tracking Vision-based ontology discovery inductive logic programming Kinect

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Can predicate invention compensate for incomplete background knowledge? 13

Can predicate invention compensate for incomplete background...

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13th Scandinavian Conference on Artificial Intelligence

作者： Cropper, Andrew Mugglteon, Stephen Imperial Coll London London England

ISBN: (纸本)9781614995890;9781614995883

In machine learning we are often faced with the problem of incomplete data, which can lead to lower predictive accuracies in both feature-based and relational machine learning. It is therefore important to develop techniques to compensate for incomplete data. In inductive logic programming (ILP) incomplete data can be in the form of missing values or missing predicates. In this paper, we investigate whether an ILP learner can compensate for missing background predicates through predicate invention. We conduct experiments on two datasets in which we progressively remove predicates from the background knowledge whilst measuring the predictive accuracy of three ILP learners with differing levels of predicate invention. The experimental results show that as the number of background predicates decreases, an ILP learner which performs predicate invention has higher predictive accuracies than the learners which do not perform predicate invention, suggesting that predicate invention can compensate for incomplete background knowledge.

关键词： inductive logic programming predicate invention relational machine learning meta-interpretive learning

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Learning Prime Implicant Conditions from Interpretation Transition 1

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24th International Conference on inductive logic programming (ILP)

作者： Ribeiro, Tony Inoue, Katsumi Grad Univ Adv Studies Sokendai Chiyoda Ku Tokyo 1018430 Japan Res Org Informat & Syst Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that J = T-P (I), where T-P is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.

关键词： Dynamical systems Boolean networks Attractors Supported models Learning from interpretation inductive logic programming

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Robust Task Execution Through Experience-based Guidance for Cognitive Robots 17

Robust Task Execution Through Experience-based Guidance for ...

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International Conference on Advanced Robotics (ICAR)

作者： Sariel, Sanem Yildiz, Petek Karapinar, Sertac Altan, Dogan Kapotoglu, Melis Istanbul Tech Univ Artificial Intelligence & Robot Lab Istanbul Turkey

ISBN: (纸本)9781467375092

Robustness in task execution requires tight integration of continual planning, monitoring, reasoning and learning processes. In this paper, we investigate how robustness can be ensured by learning from experience. Our approach is based on a learning guided planning process for a robot that gains its experience from action execution failures through lifelong experiential learning. inductive logic programming (ILP) is used as the learning method to frame hypotheses for failure situations. It provides first-order logic representation of the robot's experience. The robot uses this experience to construct heuristics to guide its future decisions. The performance of the learning guided planning process is analyzed on our Pioneer 3-AT robot. The results reveal that the hypotheses framed for failure cases are sound and ensure safety and robustness in future tasks of the robot.

关键词： Robot Task execution experience CASING FAILURE Robustness inductive logic programming Learning methods Learning processes Planning process Heuristics

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Model transformation by example using inductive logic programming

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SOFTWARE AND SYSTEMS MODELING 2009年第3期8卷 347-364页

作者： Balogh, Zoltan Varro, Daniel Budapest Univ Technol & Econ Dept Measurement & Informat Syst H-1117 Budapest Hungary OptXware Res & Dev Ltd Budapest Hungary

Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming (Muggleton and Raedt in J logic Program 19-20:629-679, 1994) which aims at the inductive construction of first-order clausal theories from examples and background knowledge.

关键词： Model transformation By-example synthesis inductive logic programming

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A phenotypic genetic algorithm for inductive logic programming

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 6935-6944页

作者： Chien, Ya-Wen Chang Chen, Yen-Liang Natl Cent Univ Dept Informat Management Jhongli 32001 Taoyuan County Taiwan

inductive logic programming (ILP) is one of the most popular approaches in machine learning. ILP can be used to automate the construction of first-order definite clause theories from examples and background knowledge. Although ILP has been successfully applied in various domains, it has the following weaknesses: (1) weak capabilities in numerical data processing. (2) zero noise tolerance, and (3) unsatisfactory learning time with a large number of arguments in the relation. This paper proposes a phenotypic genetic algorithm (PGA) to overcome these weaknesses. To strengthen the numerical data processing capabilities, a multiple level encoding structure is used that can represent three different types of relationships between two numerical data. To tolerate noise, PGA's goal of finding perfect rules is changed to finding top-k rules, which allows noise in the induction process. Finally, to shorten learning time, we incorporate the semantic roles constraint into PGA, reducing search space and preventing the discovery of infeasible rules. Stock trading data from Yahool Finance Online is used in our experiments. The results indicate that the PGA algorithm can find interesting trading rules from real data. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： inductive logic programming Phenotypic genetic algorithm Multiple level structure Semantic roles Numerical data

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Improving the efficiency of inductive logic programming systems

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SOFTWARE-PRACTICE & EXPERIENCE 2009年第2期39卷 189-219页

作者： Fonseca, Nuno A. Costa, Vitor Santos Rocha, Ricardo Camacho, Rui Silva, Fernando Univ Porto Fac Engn P-4100 Oporto Portugal Univ Porto LIAAD P-4100 Oporto Portugal Univ Porto IBMC P-4169007 Oporto Portugal Univ Porto Fac Ciencias P-4169007 Oporto Portugal Univ Porto CRACS P-4169007 Oporto Portugal

inductive logic programming (ILP) is a sub-field of machine learning that provides an excellent framework for multi-relational data mining applications. The advantages of ILP have been successfully demonstrated in complex and relevant industrial and scientific problems. However, to produce valuable models, ILP systems often require long running times and large amounts of memory. In this paper we address fundamental issues that have direct impact on the efficiency of ILP systems. Namely, we discuss how improvements in the indexing mechanisms of an underlying logic programming system benefit ILP performance. Furthermore, we propose novel data structures to reduce memory requirements and we suggest a new lazy evaluation technique to search the hypothesis space more efficiently. These proposals have been implemented in the April ILP system and evaluated using several well-known data sets. The results observed show significant improvements in running time without compromising the accuracy of the models generated. Indeed, the combined techniques achieve several order of magnitudes speedup in some data sets. Moreover, memory requirements are reduced in nearly half of the data sets. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： inductive logic programming efficiency data structures indexing

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Machine Translation Model using inductive logic programming

Machine Translation Model using Inductive Logic Programming

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IEEE International Conference on Natural Language Processing and Knowledge Engineering

作者： Hossny, Ahmad Shaalan, Khaled Fahmy, Aly Cairo Univ Fac Comp & Informat Cairo Egypt British Univ Dubai Fac Informat Dubai U Arab Emirates

ISBN: (纸本)9781424445387

Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs inductive logic programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%

关键词： Machine translation inductive logic programming rule based machine translation example based machine translation rule Induction Arabic to English

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Applying inductive logic programming to Self-Healing Problem in Grid Computing: Is it a feasible task?

Applying Inductive Logic Programming to Self-Healing Problem...

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3rd International Conference on Advanced Engineering Computing and Applications in Sciences

作者： Ferro, Mariza Mury, Antonio Roberto Schulze, Bruno Natl Lab Sci Comp ComCiDis Distributed Sci Comp Grp Petropolis RJ Brazil

ISBN: (纸本)9781424450824

Grid computing systems are extremely large and complex so, manually dealing with its failures becomes impractical. Recently, it has been proposed that the systems themselves should manage their own failures or malfunctions. This is referred as self-healing. To deal with this challenging, is required to predict and control the process through a number of automated learning and proactive actions. In this paper, we proposed inductive logic programming, a relational machine learning method, for prediction and root causal analysis that makes it possible the development of a self-healing component.

关键词： Grid Self-healing Machine learning inductive logic programming

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IMPROVED ELECTION OF CLUSTER HEADS IN LEACH

IMPROVED ELECTION OF CLUSTER HEADS IN LEACH

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IEEE/ACS International Conference on Computer Systems and Applications

作者： Chaima Bejaoui Alexandre Guitton Abdennaceur Kachouri Sfax University National School of Engineers of Sfax Road Soukra km 4 - 3038 Tunisia

ISBN: (纸本)9781509004799

Wireless Sensor Networks (WSNs) are a promising technology to monitor distant or inaccessible areas. As nodes have a limited energy supply, many routing protocols are based on a clustering mechanism: some nodes are elected as cluster heads and have to deal with most of the communication burden of the network, while the other nodes perform only simple operations. In this paper, we propose a new election mechanism with important features: it ensures that all nodes are in range of a cluster head while keeping the number of cluster heads low, it takes into account the residual energy of nodes, and it requires a small communication overhead. We compare the performance of our election mechanism with an optimal election, as well as with the election mechanism of LEACH, which is the main clustering algorithm for WSNs.

关键词： ILP LEACH WSN clustering leaching Wireless sensor networks inductive logic programming WSN gene Ganglia cluster head Head Cluster Analysis residual energy of node Routing protocols Residual energy

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