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Adaptive Difficulty

Adaptive Difficulty. By: Lewis Sykalski. The Problem The Proposal.

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Adaptive Difficulty

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  1. Adaptive Difficulty By: Lewis Sykalski

  2. The Problem The Proposal Video games get boring quickly.  Especially if they are too easy.  That's why there is the miracle of difficulty.  They breathe new life into video games by allowing the user to specify a level (eg. easy, medium, hard) that he will find challenging but at the same time not too difficult.  Remember, he must still be able to progress in the game.  This difficulty setting is often hard to discover.  Most often it comes from trial and error.  The player must keep retrying via trial and error to find his "optimum" difficulty setting, but this is sometimes a long and tedious process. Wouldn't it be cool if the computer could do the work for you? Breathing new life into games is possible, without all that tedious work that goes along with it. I propose using a neural network to solve the difficulty problem. By keeping track of any player's history, the neural network can assess the player’s skills and find the player’s "optimum" difficulty setting. I intend to demonstrate this on a game very near and dear to all gaming enthusiasts—Space Invaders.

  3. The Network I will use a MLP network to solve the difficulty problem. The input feature space is shown below as well as the sole output difficulty. I planned to try to do a one-hot implementation but I have since abandoned the idea. • High Score-the highest score this player has reached • Total Score-the total score of all games played by this player • Avg. Score-the avg. score over all games played by this player • Games- The total number of games this player has played • Level- The level reached on the last game played by this player Feature Space Output • Difficulty-The difficulty setting for this round of play

  4. Meet The Players • Lewis • The creator of this mess • Rated: Expert • Hobbies: Computer Architecture and Neural Networking • Monica • My girlfriend • Rated: Fair • Hobbies:Hockey, Cooking, and Alien Zapping The players will select what difficulty they feel most comfortable with and correlations between input variables and output difficulty will be made by the neural network. • Dave • CE Grad & Friend • Rated: Very Good • Hobbies: Graphics and Neural Networking Other players: Aaron, Chen, Paul, Greg

  5. Progress • (11/01) Developed Game, Scoring Algorithm, Difficulty Algorithm • (12/4/01) Developed methods for Saving High Score info and Player History • (12/7/01) Lots of game playing • (12/10/01) Changed Matlab code to correct config., realized needed more data

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