Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game


  • Bryon Kucharski Wentworth Institute of Technology
  • Azad Deihim Wentworth Institute of Technology
  • Mehmet Ergezer Wentworth Institute of Technology



This research was conducted by an interdisciplinary team of two undergraduate students and a faculty to explore solutions to the Birds of a Feather (BoF) Research Challenge. BoF is a newly-designed perfect-information solitaire-type game. The focus of the study was to design and implement different algorithms and evaluate their effectiveness. The team compared the provided depth-first search (DFS) to heuristic algorithms such as Monte Carlo tree search (MCTS), as well as a novel heuristic search algorithm guided by machine learning. Since all of the studied algorithms converge to a solution from a solvable deal, effectiveness of each approach was measured by how quickly a solution was reached, and how many nodes were traversed until a solution was reached. The employed methods have a potential to provide artificial intelligence enthusiasts with a better understanding of BoF and novel ways to solve perfect-information games and puzzles in general. The results indicate that the proposed heuristic search algorithms guided by machine learning provide a significant improvement in terms of number of nodes traversed over the provided DFS algorithm.




How to Cite

Kucharski, B., Deihim, A., & Ergezer, M. (2019). Machine Learning Based Heuristic Search Algorithms to Solve Birds of a Feather Card Game. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9656-9661.



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