<p> Machine learning has recently been adopted into major gaming releases after a period of lacking enthusiasm towards the field. This lack was do to the feeling that the ability for the computer to "learn" from its opponents would not be appreciated or noticed by gamers and therefore would be a waste of both time and money.</p>

<p> Machine learning has recently been adopted into major gaming releases after a period of lacking enthusiasm towards the field. This lack was do to the feeling that the ability for the computer to "learn" from its opponents would not be appreciated or noticed by gamers and therefore would be a waste of both time and money.</p>

<p> Gaming companies are now looking into the development of games that harness the power of creating a game that can learn from its opponent. Games have standardly used difficulty levels to challenge its players to become better at the game. However these difficulty settings are easly mastered by discovering a routine that will allow them to beat the computer opponent and then using that routine to repeatedly beat the computer. If machine learning is used to control these computer opponents, this unbeatable routine will begin to breakdown. The computer will begin to learn what techniques are being used (such as where the player usually hides or what forces they usually attack with) and will be able to use this to make the game not only more challenging but also different for each user. This would hopefully imporve the play-life of each game.</p>

<p> Gaming companies are now looking into the development of games that harness the power of creating a game that can learn from its opponent. Games have standardly used difficulty levels to challenge its players to become better at the game. However these difficulty settings are easly mastered by discovering a routine that will allow them to beat the computer opponent and then using that routine to repeatedly beat the computer. If machine learning is used to control these computer opponents, this unbeatable routine will begin to breakdown. The computer will begin to learn what techniques are being used (such as where the player usually hides or what forces they usually attack with) and will be able to use this to make the game not only more challenging but also different for each user. This would hopefully imporve the play-life of each game.</p>

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==Downfalls of Machine Learning==

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<b>Learned Stupidity</b>- Because the computer adapts to the playstyle of the gamer, it is possible for the AI system to "learn" the habits of an inexperienced player making it less intelligence. <br>

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<b>Overfitting</b>- The AI agent does not develop overall intelligence. If a gamer repeatily plays one level the AI agents will become very intelligent at that level, but what it has learned would not always apply to later levels causing for the learning to hinder the agent.<br>

Revision as of 15:53, 2 September 2007

Machine learning is a section of computer programming which improve automatically over time. This technique is in many real world applications including the detection of credit card fraud, speech and handwriting recognition, natural language processing and gameplaying. Machine learning covers several different sections of AI including but not limited to Evolutionary Computation and Artificial Neural Networks

Gaming

Machine learning has recently been adopted into major gaming releases after a period of lacking enthusiasm towards the field. This lack was do to the feeling that the ability for the computer to "learn" from its opponents would not be appreciated or noticed by gamers and therefore would be a waste of both time and money.

Gaming companies are now looking into the development of games that harness the power of creating a game that can learn from its opponent. Games have standardly used difficulty levels to challenge its players to become better at the game. However these difficulty settings are easly mastered by discovering a routine that will allow them to beat the computer opponent and then using that routine to repeatedly beat the computer. If machine learning is used to control these computer opponents, this unbeatable routine will begin to breakdown. The computer will begin to learn what techniques are being used (such as where the player usually hides or what forces they usually attack with) and will be able to use this to make the game not only more challenging but also different for each user. This would hopefully imporve the play-life of each game.

Downfalls of Machine Learning

Learned Stupidity- Because the computer adapts to the playstyle of the gamer, it is possible for the AI system to "learn" the habits of an inexperienced player making it less intelligence. Overfitting- The AI agent does not develop overall intelligence. If a gamer repeatily plays one level the AI agents will become very intelligent at that level, but what it has learned would not always apply to later levels causing for the learning to hinder the agent.