Artificial intelligence

Abstract: The paper summarizes the experience of teaching the "Methods and Systems of Artificial Intelligence" course to second-year Computer Science students. An analysis of the curricula for this discipline showed that the examples used to introduce important AI concepts are diverse and fragmented. We introduced a variant of Bachet’s subtraction game as a problem for students to solve by developing various types of AI agents. A simulation environment was developed to serve as a platform for intelligent agents capable of playing different variants of Bachet’s game. The techniques used to create intelligent agents for this game included: manual search for the optimal solution and hard-coding the agent; state-space search (graph search); Q-learning; genetic programming; and neural networks. While studying each technique, students developed agents required to play Bachet’s game, compete against agents created by other students, and adapt to changes in the game rules. By applying these distinct technologies to a single, consistent task, students gain a unique opportunity to benchmark performance and understand the specific advantages and limitations of each method—comparing the exactness of exhaustive search against the generalization capabilities of neural networks. Questions arising during classroom discussions of the results stimulate further research and increase student engagement and activity in studying artificial intelligence models and systems. Bachet’s game serves as an excellent example of a problem that bridges different areas of AI due to its ease of understanding, the ability to construct a mental model and a game state graph using pen and paper, its competitive nature, and its flexibility. The competitive element, combined with the analytical challenge of adapting to changing game mechanics, significantly boosts student engagement and stimulates lively classroom discussions, bridging the gap between theoretical models and practical system implementation, which gives a basis for understanding the more advanced concepts.

Keywords: genetic algorithms, neural networks, unsupervised learning, intelligent agents, education.

References:

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