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Applying machine learning and select...

Xiao, Gang.

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Applying machine learning and selective sampling techniques to game software testing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applying machine learning and selective sampling techniques to game software testing./
Author:	Xiao, Gang.
Description:	116 p.
Notes:	Source: Masters Abstracts International, Volume: 46-02, page: 1030.
Contained By:	Masters Abstracts International46-02.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR30042
ISBN:	9780494300428

Applying machine learning and selective sampling techniques to game software testing.
Xiao, Gang.

Applying machine learning and selective sampling techniques to game software testing. - 116 p.

Source: Masters Abstracts International, Volume: 46-02, page: 1030.

Thesis (M.Sc.)--University of Alberta (Canada), 2007.

Although commercial computer games usually undergo intensive testing before release, many bugs and sweet spots still exist and make games less attractive than expected. In this thesis, a Semi-Automated Gameplay Analysis (SAGA-ML) system is developed to summarize game behaviors as human readable rules, which can be presented to game designers to check if those behaviors are as intended. Unexpected game behaviors can be found this way. Machine learning and selective sampling techniques are incorporated into automated software testing. Machine learning is used to create a summary of the gameplay log that is comprehensible by humans. Selective sampling is used to sample instance space intelligently to build a good model. Four existing selective sampling algorithms (Uncertainty Sampling, Bagging, Boosting and BootStrapLV), and a new rule-based selective sampling method, are implemented and compared. SAGA-ML has been tested on Electronic Arts' FIFA99 soccer game and shown to be a practical game behavior testing solution.

ISBN: 9780494300428Subjects--Topical Terms:

626642
Computer Science.

Applying machine learning and selective sampling techniques to game software testing.
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245 1 0 $a Applying machine learning and selective sampling techniques to game software testing.
300 $a 116 p.
500 $a Source: Masters Abstracts International, Volume: 46-02, page: 1030.
502 $a Thesis (M.Sc.)--University of Alberta (Canada), 2007.
520 $a Although commercial computer games usually undergo intensive testing before release, many bugs and sweet spots still exist and make games less attractive than expected. In this thesis, a Semi-Automated Gameplay Analysis (SAGA-ML) system is developed to summarize game behaviors as human readable rules, which can be presented to game designers to check if those behaviors are as intended. Unexpected game behaviors can be found this way. Machine learning and selective sampling techniques are incorporated into automated software testing. Machine learning is used to create a summary of the gameplay log that is comprehensible by humans. Selective sampling is used to sample instance space intelligently to build a good model. Four existing selective sampling algorithms (Uncertainty Sampling, Bagging, Boosting and BootStrapLV), and a new rule-based selective sampling method, are implemented and compared. SAGA-ML has been tested on Electronic Arts' FIFA99 soccer game and shown to be a practical game behavior testing solution.
590 $a School code: 0351.
650 4 $a Computer Science. $3 626642
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773 0 $t Masters Abstracts International $g 46-02.
790 $a 0351
791 $a M.Sc.
792 $a 2007
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