Analysis of selected methods of creating artificial intelligence on the example of a popular card game
The aim of the article was to analyze selected methods of creating artificial intelligence in a popular card game. Two experiments were conducted: with a human and with a computer. The following algorithms were analyzed: random, min-max, based on a neural network, statistical and statistical with the use of "cheating" technique. The examined parameters were as follows: efficiency, execution time, number of implementation code lines, implementation time and training duration. The indicator with the greatest impact on the selection of the most optimal method was efficiency. The research has shown no difference in efficiency for the neural network-based algorithm and the statistical algorithm. In other cases, the differences in this feature were significant. The use of the "cheating" technique has increased the efficiency.
artificial intelligence; machine learning; algorithm efficiency evaluation; computer games
D.S. Cohen, T.J. Park, S.A Bustamante, Producing Games: From Business and Budgets to Creativity and Design, Routledge, 2010.
T. Walsh, To zyje! Sztuczna inteligencja, Wydawnictwo Naukowe PWN, 2018.
K. Wołk, Zabawa ze sztuczna inteligencja, Psychoskok, 2018.
S. Nowaczyk, Frontiers in Artificial Intelligence and Applications, Thirteenth Scandinavian Conference on Artificial Intelligence, Tom 278, Holandia, 2015.
T. Burczynski, W. Cholewa, W. Moczulski, Methods of artificial intelligence, Silesian University of Technology, 2009.
S. Castellano, Artificial Intelligence, Genuine Learning, Talent Development, Tom 73, Wydanie 10, 2019.
J. Łeski, Systemy neuronowo-rozmyte, Wydawnictwo WNT, 2008.
L. Rutkowski, Metody i techniki sztucznej inteligencji, Wydawnictwo Naukowe PWN, 2009.
Z. Shi, Advanced Artificial Intelligence, World Scientific, Singapur, 2011.
B. Zohuri, M. Moghaddam, Neural Network Driven Artificial Intelligence: Decision Making Based on Fuzzy Logic. Nova Science Publisher, Nowy Jork, 2017.
N. Joshi, Hands-On Artificial Intelligence with Java for Beginners: Build Intelligent Apps Using Machine Learning and Deep Learning with Deeplearning4j, Packt Publishing, 2018.
Przeglad branzy gier wideo, https://www.wepc.com/news/video-game-statistics, [13.07.2020].
G.N. Yannakakis, J. Togelius, Artificial intelligence and games, Cham: Springer, 2018.
G.N. Yannakakis, J. Togelius, A Panorama of Artificial and Computational Intelligence in Games, IEEE Transactions on Computational Intelligence and AI in Games, 2015.
D. Brackeen, Java: tworzenie gier, Wydawnictwo Helion, Gliwice, 2004.
J.S. Harbour, Beginning Java SE 6 Game Programming, MA: Course PTR, Boston, 2012.
B. Kaluza, Machine Learning in Java, Packt Publishing, 2016.
W. McAllister, J. Fritz, Programming Fundamentals Using Java: A Game Application Approach, Dulles, Virginia: Mercury Learning & Information, 2015.
S. Pieta, M. Scibisz, M. Wisniewski, Podstawy tworzenia interfejsu graficznego aplikacji desktopowych w jezyku Java, Oficyna Wydawnicza Politechniki Warszawskiej, 2019.
S.K. Aditya, P. Mohanta, V. K. Karn, Android SQLite Essentials, Packt Publishing, 2014.
R.A. Johnson, Java Database Connectivity Using Sqlite: A Tutorial, Java Database Connectivity Using Sqlite: A Tutorial, Proceedings of the Academy of Information & Management Sciences, 2014.
B. Rigal, S. Kupisz, Gry karciane, Helion, 2007.
B. Whiter, J. Kluzinski, Gry karciane, K.E. Liber 2004.
T.B. Alakus, R. Das, I. Turkoglu, An Overview of Quality Metrics Used in Estimating Software Faults, International Artificial Intelligence and Data Processing Symposium, 2019.
L. Buglione, A. Abran, Measurement Process: Improving the ISO 15939 Standard, Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement, 2014.
Dokumentacja oprogramowania CLOC, https://github.com/AlDanial/cloc, [13.07.2020].
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