Playing Card assets from mreliptik Website theme is Hyde Portrait credit to @sierra_upsidedown
This paper was valuable to me in learning about techniques for Card Game AI:
Kupferschmid, S., Helmert, M. (2007). A Skat Player Based on Monte-Carlo Simulation. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.(. (eds) Computers and Games. CG 2006. Lecture Notes in Computer Science, vol 4630. Springer, Berlin, Heidelberg. https://doi-org.ezproxy.csuci.edu/10.1007/978-3-540-75538-8_12
I used these websites to learn about the Monte Carlo Search Tree algorithm, get inspiration, and check my work:
Implementation based on GeeksForGeeks: https://www.geeksforgeeks.org/ml-monte-carlo-tree-search-mcts/
This page was helpful as well: https://towardsdatascience.com/monte-carlo-tree-search-an-introduction-503d8c04e168
I used this to compare a Python implementation of MCST: https://ai-boson.github.io/mcts/
As well as this implementation made specifically for Connect 4: https://www.harrycodes.com/blog/monte-carlo-tree-search I used this one as a template for my Ninety Nine version
This last implementation was invaluable to me for testing and learning about the algorithm
ChatGPT was a helpful tool for the development process
Poster image credit:
MCTS Algorithm: Robert Moss, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons
White Paper Screenshot: For the paper cited above, available as an abstract at that link