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References Bensusan, H., Giraud-Carrier, C. and Kennedy, C.J. (2000). A higher-order approach to meta-learning, in J. Cussens and A. Frisch (Eds.), Proceedings of the Work-in-Progress Track at the 10th International Conference on Inductive Logic Programming , Springer-Verlag, Berlin/Heidelberg, pp. 33-42. Brazdil, P., Giraud-Carrier, C., Soares, C. and Vilalta, R. (2009). Metalearning: Applications to Data Mining , Springer, Berlin/Heidelberg. Brazdil, P., Soares, C. and da Costa, J.P. (2003). Ranking learning algorithms: Using IBL and meta-learning on


This correlational study explored the relationship between creative abilities and selected meta-learning competences. The study was conducted among 250 first-year undergraduate and graduate students who solved the Test for Creative Thinking – Drawing Production and filled in the My Learning Questionnaire. The results demonstrate a statistically significant correlation between students’ awareness of their own learning and their creative abilities as well as a positive link between creative abilities and level of knowledge about human learning. These relationships were not moderated by the level of studies – the links among undergraduate and graduate students were similar in the case of self-awareness of learning and knowledge about learning.

References Bensusan, H. and Giraud-Carrier, C. (2000a). Casa batl´o is in passeig de gr´acia or how landmark performances can describe tasks, Proceedings of the ECML-00 Workshop on Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Combination, Barcelona, Spain, pp. 29-46. Bensusan, H. and Giraud-Carrier, C.G. (2000b). Discovering task neighbourhoods through landmark learning performances, Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery, Lyon, France, pp. 325-330. Bensusan, H., Giraud

.), Psychopedagogika działań twórczych. [Psycho-pedagogy of creative activities]. (pp. 233-250). Kraków: Oficyna Wydawnicza Impuls. Uszyńska-Jarmoc, J. (2012). Rozumiem siebie i szkołę, umiem i chcę się uczyć - metauczenie się dziecka w świetle wyników badań jakościowych. [I understand myself and the school, I can and I want to learn - child’s meta-learning in view of qualitative research results]. In M. Kowalik-Olubińska (Ed.), Dzieciństwo i wczesna edukacja w dynamicznie zmieniającym się świecie. [Childhood and primary education in dynamically changing world]. (pp. 256


One of the original goals of artificial intelligence (AI) research was to create machines with very general cognitive capabilities and a relatively high level of autonomy. It has taken the field longer than many had expected to achieve even a fraction of this goal; the community has focused on building specific, targeted cognitive processes in isolation, and as of yet no system exists that integrates a broad range of capabilities or presents a general solution to autonomous acquisition of a large set of skills. Among the reasons for this are the highly limited machine learning and adaptation techniques available, and the inherent complexity of integrating numerous cognitive and learning capabilities in a coherent architecture. In this paper we review selected systems and architectures built expressly to address integrated skills. We highlight principles and features of these systems that seem promising for creating generally intelligent systems with some level of autonomy, and discuss them in the context of the development of future cognitive architectures. Autonomy is a key property for any system to be considered generally intelligent, in our view; we use this concept as an organizing principle for comparing the reviewed systems. Features that remain largely unaddressed in present research, but seem nevertheless necessary for such efforts to succeed, are also discussed.

, decision making, affordance extraction, action planning and action execution (step 1). Once these powers are successfully mastered, then these systems may be embodied into a robot able to act in the real world (step 2). Their embodiment, however, cannot guarantee that these systems will be able to operate autonomously in the environment as they will still need to solve the issues of the real-time system operation, resource management and meta-learning (step 2). In their article “Cognitive architectures and autonomy: a comparative review” Thórisson and

Computational Intelligence Magazine 10 (2): 18–29. Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks, International Joint Conference on Neural Networks, Budapest, Hungary , pp. 985–990. Huang, G.-B., Zhu, Q.-Y. and Siew, C.-K. (2006). Extreme learning machine: Theory and applications, Neurocomputing 70 (1–3): 489–501. Jankowski, N. (2013). Meta-learning and new ways in model construction for classification problems, Journal of Network & Information Security 4 (4): 275–284. Jankowski, N. (2018


Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

References L. Breiman, J. Friedman, R. Olshen, C. Stone. Classification and Regression Trees. Belmont, CA: Wadsworth Int. Group, 1984. J. R. Quinlan. "Induction of Decision Trees." Machine learning, Vol. 1, Issue 1, pp. 81-106, 1986. E. Simoudis, J. Han, U. Fayyad , Eds. Second International Conference on Knowledge Discovery and Data Mining , August 2-6, 1996, Portland, Oregon, USA. Portland: AAAI Press, 1996. P. B. Brazdil, C. Soares, J. P. Da Costa. "Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results." Machine Learning

References 1. Goldberg, D. E. Genetic and Evolutionary Algorithms Come of Age. – Communications of the ACM, Vol. 37 , 1994, No 3, pp. 113-119. 2. Pappa, G., G. Ochoa, M. Hyde, A. Freitas, J. Woodward, J. Swan. Contrasting Meta-Learning and Hyper-Heuristic Research: The Role of Evolutionary Algorithms. – Genetic Programming and Evolvable Machines, Vol. 15 , 2014, Issue 1, pp. 3-35. 3. Adamidis, P. Review of Parallel Genetic Algorithms Bibliography. Tech. Rep. Version 1. Aristotle University of Thessaloniki, Thessaloniki, Greece, 1994. 4. Gordon, V. S., D