In recent years, so-called serious games, where some kind of knowledge is gained through digital game play, have received significant attention and there has been an active movement toward games that effectively enrich the learning environment. Serious games are games that are developed according to the concept, "It is possible through game play to obtain some kind of knowledge while having fun. Through these serious games, there has been an active movement toward fun learning using games in education at school and internal training at organizations. In contrast, at universities the level of learning programming differs significantly depending on the academic background of the student and there are issues with traditional programming education learning because the student does not feel enjoyment and is not interested in information engineering. As a countermeasure, teaching methods and teaching aids etc. have been cited as themes and learning through the utilization of serious games and game development has been proposed to tackle these themes. In this paper we propose and evaluate serious games for programming learning based on the concept "Learning programming through gaming".
Summary. In this paper, we propose a new type of FCSPs called hybrid domain FCSPs that have a mixture of discrete and continuous domains. To solve this type of problems, we present an algorithm called Spread-Repair based on the framework of iterative improvement. Experimental results on some test problems show that the algorithm has an ability of finding local approximate solutions with high probability in a computation time much shorter than the traditional, discrete-domain FCSP.
In this paper, we provide a new approach to classify and recognize the acoustic events for multiple autonomous robots systems based on the deep learning mechanisms. For disaster response robotic systems, recognizing certain acoustic events in the noisy environment is very effective to perform a given operation. As a new approach, trained deep learning networks which are constructed by RBMs, classify the acoustic events from input waveform signals. From the experimental results, usefulness of our approach is discussed and verified.
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