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dc.contributor.authorLong, Kathryn Anna
dc.date.accessioned2009-06-08T19:46:39Z
dc.date.available2009-06-08T19:46:39Z
dc.date.issued2009-05-04
dc.identifier.urihttp://hdl.handle.net/1853/28284
dc.description.abstractCurrently many game artificial intelligences attempt to determine their next moves by using a simulator to predict the effect of actions in the world. However, writing such a simulator is time-consuming, and the simulator must be changed substantially whenever a detail in the game design is modified. As such, this research project set out to determine if a version of the first order inductive learning algorithm could be used to learn rules that could then be used in place of a simulator. By eliminating the need to write a simulator for each game by hand, the entire Darmok 2 project could more easily adapt to additional real-time strategy games. Over time, Darmok 2 would also be able to provide better competition for human players by training the artificial intelligences to play against the style of a specific player. Most importantly, Darmok 2 might also be able to create a general solution for creating game artificial intelligences, which could save game development companies a substantial amount of money, time, and effort.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectFirst order inductive learningen
dc.subjectFirst order inductive learneren
dc.subjectFOILen
dc.subjectDarmok 2en
dc.subjectLearningen
dc.subjectGame artificial intelligenceen
dc.titleUsing First Order Inductive Learning as an Alternative to a Simulator in a Game Artificial Intelligenceen
dc.typeUndergraduate Thesisen
dc.contributor.departmentComputer Science
dc.description.advisorRam, Ashwin - Faculty Mentor ; Ontañón, Santi - Committee Member/Second Reader


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