Date Approved
2-14-2017
Embargo Period
2-15-2017
Document Type
Thesis
Degree Name
MS Computer Science
Department
Computer Science
College
College of Science & Mathematics
Advisor
Tinkham, Nancy L.
Committee Member 1
Hnatyshin, Vasil Y.
Committee Member 2
Hristescu, Gabriela
Keywords
AI, Arimaa, Artificial Intelligence, board games, Game-playing AI
Subject(s)
Artificial intelligence--Computer programs; Board games
Disciplines
Artificial Intelligence and Robotics
Abstract
The game of Arimaa was invented as a challenge to the field of game-playing artificial intelligence, which had grown somewhat haughty after IBM's supercomputer Deep Blue trounced world champion Kasparov at chess. Although Arimaa is simple enough for a child to learn and can be played with an ordinary chess set, existing game-playing algorithms and techniques have had a difficult time rising up to the challenge of defeating the world's best human Arimaa players, mainly due to the game's impressive branching factor. This thesis introduces and analyzes new algorithms and techniques that attempt to recognize similar board states based on relative piece strength in a concentrated area of the board. Using this data, game-playing programs would be able to recognize patterns in order to discern tactics and moves that could lead to victory or defeat in similar situations based on prior experience.
Recommended Citation
Ahmed, Malik Khaleeque, "Exploring algorithms to recognize similar board states in Arimaa" (2017). Theses and Dissertations. 2360.
https://rdw.rowan.edu/etd/2360