SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Dynamics of complex systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rough-Neuro-Computing: Techniques for Computing with Words
Rough-Neuro-Computing: Techniques for Computing with Words
Toward the Formal Foundation of Ant Programming
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Practical Statistics for Medical Research
Practical Statistics for Medical Research
Behavioral pattern identification through rough set modelling
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
A Domain Knowledge as a Tool For Improving Classifiers
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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The problem considered is how to model perception and identify behavioral patterns of objects changing over time in complex dynamical systems. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzis?aw Pawlak during the early 1980s provides the foundation for the construction of classifiers, relative to what are known as temporal pattern tables. Temporal patterns can be treated as features that make it possible to approximate complex concepts. This article introduces some rough set tools for perception modeling that are developed for a system for modeling networks of classifiers. Such networks make it possible to identify behavioral patterns of objects changing over time. They are constructed using an ontology of concepts delivered by experts that engage in approximate reasoning about concepts embedded in such an ontology. We also present a method that we call a method for on-line elimination of non-relevant parts (ENP). This method was developed for on-line elimination of complex object parts that are irrelevant for identifying a given behavioral pattern. The article includes results of experiments that have been performed on data from a vehicular traffic simulator and on medical data obtained from Neonatal Intensive Care Unit in the Department of Pediatrics, Collegium Medicum, Jagiellonian University. The contribution of this article is the introduction of a network of classifiers that make it possible to identify the behavioral patterns of objects that change over time.