ATRE

ATRE





Apprendimento di Teorie Ricorsive da Esempi


 
A short description 
Experimental results:
Experiment on the urban domain 
Experiment on the family domain 
Experiment on the document image understanding domain 
Experiment on the cognitive modeling domain 
Learning odd and even 
Learning can_rich and cyclic 
Learning geometric figures 
Learning corrective rules for global layout analysis 
Learning tagging rules for biomedical information extraction 
The distribution package 
Related publications 
Aknowledgements 
Contact 



A short description

ATRE is an ILP system that can learn recursive theories from examples.
In particular, the problem solved by ATRE is the following:
Given
a set of concepts K1, K2, ..., Kr to be learned,
a set of objects O described in a language LO,
a background knowledge BK described in a language LBK,
a language of hypotheses LH that defines the space of hypotheses SH
a user's preference criterion PC,
Find
a (possibly recursive) logical theory T, defining the concepts K1, K2,..., Kr, such that T is complete and consistent with respect to the set of observations and satisfies the preference criterion PC.
 

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Experiment on the urban domain

The problem is that of recognizing in a map downtown areas and residential areas.

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Experiments on the family domain

These experiments refer to the domain of family relations used to test the MPL system. Click here to see the genealogic tree.

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Experiment on the document image understanding domain

The problem is that of learning rules for document image understanding. Concepts correspond to the distinct logical components to be recognized in a document image. They are defined by different values taken by the descriptor logic_type. The unlabelled layout objects act as counterexamples for all the concepts to be learned, since they are instances of the concept logic_type(X)=undefined.
Each training document is represented as an object in ATRE, where different constants represent distinct layout components of a page. Training examples are generated by means of the WISDOM++ system. Three long papers, which appeared in the January 1996 issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), have been considered.
Experimental results are obtained for a beam equal to 15.
 

In order to test the predictive accuracy of the learned theory, we considered the fourth long article published in the same issue of the transactions used for training.
 

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Experiment on the cognitive modeling domain

ATRE has been applied to the problem of acquiring cognitive models in the context of the project LHM (Learning in Humans and Machines), Task Force 1 (Representation Change), Pilot Project 1 (Computational-Cognitive Modeling of Representational Change in Elementary Dynamics), funded by the European Science Foundation. The goal is that of checking whether it is possible to use machine learning systems to reproduce the behaviour of the mental models on the development of the concept force, proposed by cognitive psychologists and derived from experimentation on children aged eleven or younger (Ioannides & Vosniadou, 1991). In order to build a theory corresponding to the mental models, we provided ATRE with a set of observations extracted from a set of exercises or situation problems that had been presented to different classes of children. Information regarding the kinetic state of an object were obtained thorugh observation in combination with verbal statement. The typical responses were categorized based upon the children's interpretation. The goal is that of learning four distinct properties of physical objects, namely when they are stationary, when they are moving, when they have inner force and when they have an acquired force. The conjecture that inner and acquired forces exist in objects as their properties is not directly related to any observation and verbal statement. Children never hear the words inner or acquired force, they only hear the word force in different contexts. The remarkable thing is that the same word and the same context may give rise to interpretations of inner or acquired force depending on the prior knowledge of the observer.
Each scene may actually describe the property of more than one physical entity, in which case the object-centered representation adopted in ATRE proves appropriate to describe scenes as a whole. The BK provided to the system concerns the facts that earth and stones are instances of natural inanimate objects, while cars, tables, sledges and balls are instances of artifacts, which are still inanimate objects. The only animate objects specified in the background knowledge are men and horses.
 

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Learning odd and even

The experiment aims at learning the definitions of odd and even numbers. The problem is that of learning the concepts odd(_)=true and even(_)=true given the predicates succ/2 and zero/1.
 
 
 

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Learning can_rich and cyclic

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Learning geometric figures

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Learning corrective rules for global layout analysis

This learning task is described in the ICML'03 paper "Learning Logic Programs for Layout Analysis Correction". The problem is that of learning the concepts group(_,_)=.true, split(_)=.vertical, split(_)=.horizontal. Experimental results are obtained for a six-fold cross validation.
 
 
 
 

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Learning tagging rules for biomedical information extraction

This learning task is described in the ILP'06 paper "Learning Recursive Patterns for Biomedical Information Extraction". The problem is that of learning tagging rules for the biomedical literature. Concepts correspond to the distinct annotation tags to be recognized in a text. They are defined by different values taken by the descriptor annotation. The untagged tokens act as counterexamples for all the concepts to be learned, since they are instances of the concept annotation(X)=no_tag. Training examples are generated by means of the BEE module . Pubblications collected for the annotation of the HmtDB genomic database have been considered. Experimental results are obtained for a six-fold cross validation.
 
 
 

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The distribution package

There are three distribution packages available (all for Microsoft Windows platforms):
Package Required space on HD Description
VisualATRE 2.1 (5495 KB) 20 MB This package includes both the ATRE learning engine (ver. 2.3.0) and the GUI VisualATRE (ver. 2.1). ATRE has been developed in Sicstus Prolog 3.8.6 and VisualATRE has been developed in Visual C++ 6.0.
The major novelty in this release is the insertion of DATAGEN (DATA GENerator), a pre-processing component that aims at automatically generating the training and testing datasets for both the induction and the validation of logical theories. Given a training data file collecting both the training and the testing examples as well as the learning parameters, DATAGEN generates training and testing files for ATRE according to the Leave One Out, Splitting or K-Folding testing method.
Please, consult the Quick user guide of ATRE to learn more about the basic ATRE/VisualATRE usage. For a complete guide on the system, refer to on-line help provided with the system.
After downloading the file, unzip it into a temporary folder and run SETUP.EXE to install the program.
ATRE 2.3.0 (1086 KB) 3.05 MB This package includes the last release of ATRE learning engine. The major novelty in this release is the new capability of automatically correcting the training data file in case of duplicate literals and reporting the presence of errors (variables or inconsistencies in the data). In addition, this release of ATRE is able to prevent the induction of layered theories by evaluating the training examples coverage in advance.
Download and unzip the file into a folder on HD. Run the program in line command mode.
ATRE 2.3.0 (no cache) (1081 KB) 3.04 MB This package includes the version of ATRE without the caching capabilities. This version is less memory consuming but slower with respect to the version of system including caching techniques employment.
Download and unzip the file into a folder on HD. Run the program in line command mode.


Warning: Both the systems ATRE and VisualATRE are free for evaluation, research and teaching purposes, but not for commercial purposes.
Please Acknowledge
 

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Related publications

  • M. Berardi, & D. Malerba (2007). Learning Recursive Patterns for Biomedical Information Extraction. Proceedings of 16th International Conference on Inductive Logic Programming (ILP 2006), Santiago de Compostela, Spain.
  • M. Berardi, A. Varlaro & D. Malerba (2004). On the effect of caching in recursive theory learning. Proceedings of 14th International Conference on Inductive Logic Programming (ILP 2004), Porto, Portugal.
  • A. Varlaro, M. Berardi & D. Malerba (2004). Learning recursive theories with the separate-and-parallel conquer strategy. Proceedings of the Workshop on Advances in Inductive Rule Learning in conjunction with ECML/PKDD 2004, 179-193, Pisa, Italy.
  • A. Varlaro, M. Berardi & D. Malerba (2004). Improving efficiency of recursive theory learning. Convegno Italiano di Logica Computazionale (CILC 2004), 220-234, Parma, Italy.
  • D. Malerba (2003). Learning Recursive Theories in the Normal ILP Setting, Fundamenta Informaticae, 57, 1, 39-77.
  • M. Berardi, M. Ceci, F. Esposito & D. Malerba (2003). Learning Logic Programs for Layout Analysis Correction, Proceedings of the 20th International Conference on Machine Learning (ICML2003), 27-34.
  • D. Malerba, F. Esposito, F.A. Lisi & O. Altamura (2001). Automated Discovery of Dependencies Between Logical Components in Document Image Understanding, Proceedings of the Sixth International Conference on Document Analysis and Recognition, 174-178, IEEE Computer Society Press, Los Vaqueros, CA.
  • D. Malerba, F. Esposito, A. Lanza, & F.A. Lisi (2001). Machine learning for information extraction from topographic maps. In H. J. Miller & J. Han (Eds.), Geographic Data Mining and Knowledge Discovery, 291-314, Taylor and Francis, London, UK.
  • F. Esposito, D. Malerba, & F.A. Lisi (2000). Induction of recursive theories in the normal ILP setting: issues and solutions, in J. Cussens and A. Frisch (Eds.) Inductive Logic Programming, Lecture Notes in Artificial Intelligence, 1866, 93-111, Springer, Berlin, Germany.
  • D. Malerba, F. Esposito, & F.A. Lisi (1998). Learning recursive theories with ATRE, in H. Prade (Ed.), Proceedings of the 13th European Conference on Artificial Intelligence, 435-439, John Wiley & Sons, Chichester, England.
     

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    Aknowledgements

    Project Leader

    Current Staff

    Previous collaborators



    Contact

    Name Email address Tel. number Fax
    Antonio Varlaro varlaro@di.uniba.it +39 080 5442299 +39 080 5443269
     

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