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Introducing possibilistic logic in ILP for dealing with exceptions

ARTIFICIAL INTELLIGENCE 2007年第16-17期171卷 939-950页

作者： Serrurier, Mathieu Prade, Henri UPS IRIT Toulouse France

In this paper we propose a new formalization of the inductive logic programming (ILP) problem for a better handling of exceptions. It is now encoded in first-order possibilistic logic. This allows us to handle exceptions by means of prioritized rules, thus taking lessons from non-monotonic reasoning. Indeed, in classical first-order logic, the exceptions of the rules that constitute a hypothesis accumulate and classifying an example in two different classes, even if one is the right one, is not correct. The possibilistic formalization provides a sound encoding of non-monotonic reasoning that copes with rules with exceptions and prevents an example to be classified in more than one class. The benefits of our approach with respect to the use of first-order decision lists are pointed out. The possibilistic logic view of ILP problem leads to an optimization problem at the algorithmic level. An algorithm based on simulated annealing that in one turn computes the set of rules together with their priority levels is proposed. The reported experiments show that the algorithm is competitive to standard ILP approaches on benchmark examples. (c) 2007 Elsevier B.V. All rights reserved.

关键词： inductive logic programming non-monotonic reasoning possibilistic logic

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Fast frequent querying with lazy control flow compilation

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THEORY AND PRACTICE OF logic programming 2007年第4期7卷 481-498页

作者： Troncon, Remko Janssens, Gerda Demoen, Bart Vandecasteele, Henk Katholieke Univ Leuven Dept Comp Sci Louvain Belgium

Control flow compilation is a hybrid between classical WAM compilation and meta-call, limited to the compilation of non-recursive clause bodies. This approach is used successfully for the execution of dynamically generated queries in an inductive logic programming setting (ILP). Control flow compilation reduces compilation times up to an order of magnitude, without slowing down execution. A lazy variant of control flow compilation is also presented. By compiling code by need, it removes the overhead of compiling unreached code (a frequent phenomenon in practical ILP settings), and thus reduces the size of the compiled code. Both dynamic compilation approaches have been implemented and were combined with query packs, an efficient ILP execution mechanism. It turns out that locality of data and code is important for performance. The experiments reported in the paper show that lazy control flow compilation is superior in both artificial and real life settings.

关键词： logic programming inductive logic programming Warren Abstract Machine compilation

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Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation 13

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13th International Conference on Artificial Intelligence in Education (AIED 2007)

作者： Matsuda, Noboru Cohen, William W. Sewall, Jonathan Lacerda, Gustavo Koedinger, Kenneth R. Carnegie Mellon Univ Human Comp Interact Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781586037642

SimStudent is a machine-learning agent that learns cognitive skills by demonstration. SimStudent was originally built as a building block for Cognitive Tutor Authoring Tools to help an author build a cognitive model without significant programming. In this paper, we evaluate a second use of SimStudent, viz., student modeling for Intelligent Tutoring Systems. The basic idea is to have SimStudent observe human students solving problems. It then creates a cognitive model that can replicate the students' performance. If the model is accurate, it would predict the human students' performance on novel problems. An evaluation study showed that when trained on 15 problems, SimStudent accurately predicted the human students' correct behavior on the novel problems more than 80% of the time. However, the current implementation of SimStudent does not accurately predict when the human students make errors.

关键词： Cognitive modeling programming by demonstration inductive logic programming Cognitive Tutor intelligent authoring

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Learning directed probabilistic logical models: Ordering-search versus structure-search

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18th European Conference on Machine Learning (ECML 2007)/11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007)

作者： Fierens, Daan Ramon, Jan Bruynooghe, Maurice Blockeel, Hendrik Katholieke Univ Leuven Dept Comp Sci Celestijnenlaan 200A B-3001 Heverlee Belgium

ISBN: (纸本)9783540749578

We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we propose to upgrade another algorithm, namely ordering-search, since for Bayesian networks this was found to work better than structure-search. We experimentally compare the two upgraded algorithms on two relational domains. We conclude that there is no significant difference between the two algorithms in terms of quality of the learnt models while ordering-search is significantly faster.

关键词： statistical relational learning probabilistic logical models inductive logic programming Bayesian networks probability trees structure learning

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Distributed generative data mining

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7th Industrial Conference on Data Mining

作者： Ramos, Ruy Camacho, Rui LIACC Rua Ceuta 118-6 P-4050190 Oporto Portugal EEUP P-4200465 Oporto Portugal

ISBN: (纸本)9783540734345

A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts. In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic. To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an inductive logic programming system (IndLog) used in a Relational Data Ming task. As a proof-of-concept, the extended system was used to analyse an artificial dataset of a credit scoring problem with eighty million records.

关键词： data mining parallel and distributed computing inductive logic programming

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A clause learning algorithm combining immune mechanism to invent predicate

A clause learning algorithm combining immune mechanism to in...

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6th International Conference on Machine Learning and Cybernetics

作者： Yu, Peng Liu, Da-You Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

ISBN: (纸本)9781424409723

Aiming at the larger search space of learning clause in inductive logic programming, we put forward the definitions of the predicate template and the clause template to reduce search space. We suggest IMPI algorithm, which is a genetic algorithm combining immune mechanism and uses the clause template as genetic code to learn the clause template of required clause. IMPI uses immune mechanism to invent new predicates, which can extend the hypothesis language, find better results. Correspondingly we design when to invent predicates. After obtaining the clause template, we use a general method based on generalization and information gain sampling to convert clause template to clause. We design the corresponding fitness function and genetic operator . it indicates that this algorithm can reduce the search space, improve the efficiency of search algorithm and can learn recursion clause by theoretical analysis and experiment comparison. It is an effective clause learning algorithm.

关键词： inductive logic programming clause template immune mechanism recursion clause information gain genetic algorithm

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Temporal abstraction and inductive logic programming for arrhythmia recogniton from electrocardiograms

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2003年第3期28卷 231-263页

作者： Carrault, G Cordier, MO Quiniou, R Wang, F IRISA INRIA F-35042 Rennes France Univ Rennes 1 IRISA F-35042 Rennes France LTSI F-35042 Rennes France

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable. (C) 2003 Elsevier B.V. All rights reserved.

关键词： ECG medical data analysis cardiac arrhythmia classification temporal abstraction artificial neural network inductive logic programming chronicle recognition

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Recognition of hand-printed characters based on structural description and inductive logic programming

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PATTERN RECOGNITION LETTERS 2003年第16期24卷 3187-3196页

作者： Amin, A Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. The main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example. The system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the average correct recognitions rate obtained using cross-validation was 86.65%. (C) 2003 Elsevier B.V. All rights reserved.

关键词： pattern recognition Arabic characters hand-printed characters parallel thinning feature extraction structural classification inductive logic programming

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A CLAUSE LEARNING ALGORITHM COMBINING IMMUNE MECHANISM TO INVENT PREDICATE

A CLAUSE LEARNING ALGORITHM COMBINING IMMUNE MECHANISM TO IN...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： PENG YU DA-YOU LIU College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Uni

Aiming at the larger search space of learning clause in inductive logic programming, we put forward the definitions of the predicate template and the clause template to reduce search *** suggest IMPI algorithm, which is a genetic algorithm combining immune mechanism and uses the clause template as genetic code to learn the clause template of required *** uses immune mechanism to invent new predicates, which can extend the hypothesis language, find better *** we design when to invent *** obtaining the clause template, we use a general method based on generalization and information gain sampling to convert clause template to *** design the corresponding fitness function and genetic *** indicates that this algorithm can reduce the search space, improve the efficiency of search algorithm and can learn recursion clause by theoretical analysis and experiment *** is an effective clause learning algorithm.

关键词： inductive logic programming Clause template Immune mechanism Recursion clause Information gain Genetic algorithm

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Exploration of picture grammars, grammar learning and inductive logic programming for image understanding

Exploration of picture grammars, grammar learning and induct...

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Conference on Automatic Target Recognition XIII

作者： Ducksbury, PG Kennedy, C Lock, Z QinetiQ Adv Proc Ctr Malvern WR14 3PS Worcs England

ISBN: (纸本)0819449547

Grammars have been used for the formal specification of programming languages [1], and there are a number of commercial products which now use grammars. However, these have tended to be focused mainly on flow control type applications. In this paper, we consider the potential use of picture grammars and inductive logic programming in generic image understanding applications, such as object recognition. A number of issues are considered, such as what type of grammar needs to be used, how to construct the grammar with its associated attributes, difficulties encountered with parsing grammars followed by issues of automatically learning grammars using a genetic algorithm. The concept of inductive logic programming is then introduced as a method that can overcome some of the earlier difficulties.

关键词： grammars genetic algorithms inductive logic programming object recognition feature detection

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