Abstract
Cognitive agents should be equipped with computational methods enabling autonomous extraction of internal cognitive structures, i.e., supporting organization of large datasets and facilitating interaction with human users. In this research, we focus solely on the extraction of basic-level categories. We propose a computational approach that allows the agent to develop, through individual interaction with the external world, provisional clusters (using hierarchal clustering techniques) and filter them, using predefined measures of basic-levelness (inspired by psycholinguistic research). We focus on analysing the behaviour of two proposed computational approaches, namely dendrogram and multidendrogram, to establishing provisional clusters. Further, the approach is extensively studied through an array of simulations. Obtained results highlight that the proposed methods can be used to extract basic-level categories.
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Notes
- 1.
Basic binarization process was performed for non-binary attributes.
- 2.
Value should be minimized for AS measure and maximized for remaining ones.
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Acknowledgment
This research was carried out at Wrocław University of Science and Technology (Poland) under Grant 0401/0190/18 titled Models and Methods of Semantic Communication in Cyber-Physical Systems.
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Mulka, M., Lorkiewicz, W., Katarzyniak, R.P. (2020). Extraction of Basic-Level Categories Using Dendrogram and Multidendrogram. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_25
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