Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences
The two-volume set LNCS 11508 and 11509 constitutes the refereed proceedings of of the 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019, held in Zakopane, Poland, in June 2019.
The 122 revised full papers presented were carefully reviewed and selected from 333 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation.
The papers included in the second volume are organized in the following five parts: computer vision, image and speech analysis; bioinformatics, biometrics, and medical applications; data mining; various problems of artificial intelligence; agent systems, robotics and control.
The paper defines an annotated suffix tree (AST) - a data structure used to calculate and store the frequencies of all the fragments of the given string or a collection of strings. The AST is associated with a string to text scoring, which takes all fuzzy matches into account. We show how the AST and the AST scoring can be used for Natural Language Processing tasks. Copyright © by the paper's authors. Copying only for private and academic purposes.
This paper presents a clustering algorithm, namely MFWK-Means, which is a novel extension of K-Means clustering to the case of fuzzy clusters and weighted features. First, the Weighted K-Means criterion utilizing Minkowski metric is adopted to solve the problem of feature selection for high dimensional data. Then, a further extension to the case of fuzzy clustering is presented to group datasets with natural fuzziness of cluster boundaries. Also, we adopt an intelligent version of K-Means, using Mirkin’s method of Anomalous Pattern for initialization. Our new Minkowski metric Fuzzy Weighted K-Means (MFWK-Means) is experimentally validated on both benchmark datasets and synthetic datasets. MFWK-Means is shown to be competitive and more stable against noise in comparison with a variety of versions of K-Means based methods. Moreover, in most situations it reaches the highest clustering accuracy at wider intervals of Minkowski exponent.
An experimental approach was created for the comparative investigation of the cognitive abilities of the glaucous-winged gull (Larus glaucescens) in their natural habitat. The territoriality of gulls during the breeding period and the fact that the gulls inhabiting the territory of the Komandorsky Reserve are practically not in fear of humans allowed us to work with individually recognized birds directly at their nest sites inside the colony. The possibility of using this approach to investigate their cognitive abilities was demonstrated on 24 gulls, in particular, to investigate their abilities for relative size generalization. The first experiment illustrated that the gulls are able to learn to discriminate two pairs of stimuli according to the feature: 'larger' or 'smaller'. They were then given a test to transfer the discriminative rule in which novel combinations of the same stimuli were used. The gulls successfully coped with only a few of these tests. In the next experiment the birds were taught to discriminate four pairs of similar stimuli. The majority of the birds coped with the tests to transfer the discriminative rule both to the novel combinations of familiar stimuli, and also to the novel stimuli of the familiar category (items of different colour and shape). However, none of the birds transferred the discriminative rule to stimuli of a novel category (sets differing by number of components). Thus, in their ability to generalize at a preconceptual level gulls are more comparable with pigeons, whereas large-brained birds (crows and parrots), are capable of concept formation.