Description
Assignment 9. GUI-Based Rock-Paper-Scissors Game
Points: 100
GUI-Based Rock-Paper-Scissors Game
Use wxWidgets to create a GUI-based version of a Rock-Paper-Scissors game
program. Required features:
• For each round, there is a way for the human player to choose rock, paper, or
scissors. The computer makes a random choice. Display the human’s and the
computer’s choices.
• Display who won each round (or was it a tie).
• Display a running count of human wins, computer wins, and ties.
• The default is 20 rounds per game. Provide a way to change that number.
• Required menu commands:
o About
o Exit
o Start a new game
Written report
In a short report, describe:
• What events does your game application generate?
• How did you use callback functions to handle the events?
Include a screen shot of your GUI in your report.
What to turn in
Make a zip file of all your C++ source files and your report.
Submit it into Canvas: Assignment #9. GUI-Based Rock-Paper-Scissors Game
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Rubric
Your program will be graded according to these criteria:
Criteria Max points
• GUI components:
o Which round
o A way for the user to enter a choice for each round.
o The computer’s choice for the round.
o Who the winner is (or is it a tie) of the round.
o The number of human and computer wins, and the number of ties.
• Menu items:
o About
o Exit
o Start a new game
• An interactive way to change the number of rounds per game.
• The grader is able to play several games.
• 60
o 10
o 20
o 10
o 10
o 10
• 15
o 5
o 5
o 5
• 10
• 15
Extra credit: RPS Game with simple machine learning
Human players of the Rock Paper Scissors game try to develop strategies to beat their
opponents. Therefore, humans generally do not make random choices. Instead, their
choices exhibit patterns that a computer can discover and exploit using a simple
machine learning algorithm.
The computer’s ML choice algorithm
Continuously record the last N choices between the human and the computer player.
Throw out the oldest choice in order to add a new one. For example, suppose N = 5 and
during the game, the recorded choices are (the human’s choices are underlined):
PSRSP
The last choice was made by the human, and it was paper.
For each recorded sequence that ends with the human’s choice, the computer should
store how many times that sequence has occurred (each sequence’s frequency).
For example, for N = 5, some of the stored sequences and their frequencies may be
(in no particular order, the human choices are underlined):
RSPSR:1, SPRPP:3, RSPRS:2, RSPSS:4, RSPSP:3, SSRPP:2
Suppose that in the previous round, the human chose rock and the computer chose
scissors. In the current round, the human chose paper and the computer chose scissors.
Therefore, the last four choices were RSPS.
The computer can predict that the human will most likely next choose scissors, since
RSPSS appears more times (4) than RSPSR (1, predict rock) and RSPSP (3, predict
paper) in the stored frequencies. Therefore, the computer should choose rock to beat
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the human’s predicted choice of scissors. After the human makes a choice, update the
appropriate frequency.
If a sequence is not in the store, then the computer can make a random choice.
As the computer plays more games and stores more frequency data, it becomes
increasingly more capable of predicting the human’s next choice. Over the long haul, the
computer will become very difficult for humans to beat.
Example screenshot
This screenshot includes the extra credit. The computer made the correct prediction that
the human would choose paper for this round and therefore it chose scissors to win.