Comparing Efficiency of the Minimax Algorithm to Alternatives
Session Number
F13
Advisor(s)
Phadmakar Patankar, Illinois Mathematics and Science Academy
Location
A-115
Start Date
28-4-2016 8:50 AM
End Date
28-4-2016 9:15 AM
Abstract
Digital technology has become increasingly important in our lives. While many programs have a set capability, we have begun using machines that learn using adaptive algorithms, allowing computers to imitate the flexibility of the human brain, reacting quicker and more effectively. This investigation could provide rationale for alternatives to brute force algorithms. We started by coding a connect four game in Java, implementing a brute force algorithm called Minimax, and developed a version that used limited resources as a foundation for our AI (artificial intelligence). Our data analysis compares the win-loss ratio of the AI to the hypothetical perfect record of Minimax. Preliminary testing suggests that Minimax is far less efficient than AI alternatives; Minimax was unable to calculate a single move in fourteen hours, while the AI takes significantly less time. One of our alternatives to brute force is an algorithm that uses large quantities of random sampling to determine the most effective turn, rather than simulating all possible combinations; This algorithm is slower than one that makes random moves, but more effective. The improved performance demonstrated by our AI could show alternatives to brute force algorithms in real world applications, trading calculation time and effectiveness for increased efficiency.
Comparing Efficiency of the Minimax Algorithm to Alternatives
A-115
Digital technology has become increasingly important in our lives. While many programs have a set capability, we have begun using machines that learn using adaptive algorithms, allowing computers to imitate the flexibility of the human brain, reacting quicker and more effectively. This investigation could provide rationale for alternatives to brute force algorithms. We started by coding a connect four game in Java, implementing a brute force algorithm called Minimax, and developed a version that used limited resources as a foundation for our AI (artificial intelligence). Our data analysis compares the win-loss ratio of the AI to the hypothetical perfect record of Minimax. Preliminary testing suggests that Minimax is far less efficient than AI alternatives; Minimax was unable to calculate a single move in fourteen hours, while the AI takes significantly less time. One of our alternatives to brute force is an algorithm that uses large quantities of random sampling to determine the most effective turn, rather than simulating all possible combinations; This algorithm is slower than one that makes random moves, but more effective. The improved performance demonstrated by our AI could show alternatives to brute force algorithms in real world applications, trading calculation time and effectiveness for increased efficiency.