Making and Acting on Predictions in StarCraft: Brood War

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dc.contributor.authorAlburg, Henrik
dc.contributor.authorBrynfors, Filip
dc.contributor.authorMinges, Florian
dc.contributor.authorPersson Mattsson, Björn
dc.contributor.authorSvensson, Jakob
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T13:30:55Z
dc.date.available2019-07-03T13:30:55Z
dc.date.issued2014
dc.description.abstractMaking predictions and strategic decisions is not just a problem in real life, but also in the complex environments of most real-time strategy games. Due to the lack of complete information, such games can be used as testbeds for AI research in this area. This thesis presents how an AI agent, in the game StarCraft, can make use of a Bayesian network to predict the state of the opposing player's technology tree. It is also shown how the predictions can be used to assist the AI agent in making strategic decisions. Bayesian networks are used for the predictions, and the agent's army composition is generated dynamically based on the predictions and the observed opponent units. The agent is tested against StarCraft's built-in AI. The results show that it is possible to accurately predict the state of the opponent's technology tree, and that the predictions have a positive e ect on the AI agent's win rate.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/203120
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleMaking and Acting on Predictions in StarCraft: Brood War
dc.type.degreeExamensarbete för kandidatexamensv
dc.type.degreeBachelor Thesisen
dc.type.uppsokM2
local.programmeSoftware Engineering (300 hp)
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