Opponent Modeling and Strategic Reasoning in the Real-time Strategy Game Starcraft
Since the release of BWAPI in 2009, StarCraft has taken the position as the leading platform for research in artificial intelligence in real-time strategy games. With competitions being held annually at AIIDE and CIG, there is much prestige in having an agent compete and do well. This thesis is aimed at presenting a model for doing opponent modeling and strategic reasoning in StarCraft.
We present a method for constructing a model based on strategies, on the form of build orders, learned from expert demonstrations. This model is aimed at recognizing the strategy of the opponent and selecting a strategy that is capable of countering the recognized strategy. The method puts weight on the ordering and timing of buildings in order to do advanced recognition.
Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2012. , 97 p.
ntnudaim:7163, MTDT datateknikk, Intelligente systemer
IdentifiersURN: urn:nbn:no:ntnu:diva-18449Local ID: ntnudaim:7163OAI: oai:DiVA.org:ntnu-18449DiVA: diva2:565941
Langseth, Helge, ProfessorKofod-Petersen, Anders