On time and space decomposition of complex structures
Communications of the ACM
Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Transformations and decompositions of nets
Advances in Petri nets 1986, part I on Petri nets: central models and their properties
The complexity of Boolean functions
The complexity of Boolean functions
Machine learning: a theoretical approach
Machine learning: a theoretical approach
An Automated Approach to Information Systems Decomposition
IEEE Transactions on Software Engineering
A parallel algorithm for real-time decision making: a rough set approach
Journal of Intelligent Information Systems
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Introduction: Cognitive Autonomy in Machine Discovery
Machine Learning
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Automatic Theorem Generation in Plane Geometry
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
An Incremental Learning Algorithm for Constructing Decision Rules
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Rough Sets and Knowledge Discovery: An Overview
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Discovery of equations: experimental evaluation of convergence
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Design automation tools for efficient implementation of logic functions by decomposition
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A Rough Set Approach to Information Systems Decomposition
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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The main objective of machine discovery is the determination of relations between data and of data models. In the paper we describe a method for discovery of data models represented by concurrent systems from experimental tables. The basic step consists in a determination of rules which yield a decomposition of experimental data tables; the components are then used to define fragments of the global system corresponding to a table. The method has been applied to automatic data models discovery from experimental tables with Petri nets as models for concurrency.