Avancerad sökning

Hittade 1 uppsats som matchar ovanstående sökkriterier.

1. 1. Clustering and Classification of Time Series in Real-Time Strategy Games - A machine learning approach for mapping StarCraft II games to clusters of game state time series while limited by fog of war

   Kandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Olof Enström; Fredrik Hagström; John Segerstedt; Fredrik Viberg; Arvid Wartenberg; David Weber Fors; [2020-10-29]
   Nyckelord :Classification problem; Cluster analysis; Hierarchical clustering; Machine learning; Neural network; Random forest; Real-time strategy; StarCraft II; Time series;

   Sammanfattning : Real-time strategy (RTS) games feature vast action spaces and incomplete information, thus requiring lengthy training times for AI-agents to master them at the level of a human expert. Based on the inherent complexity and the strategical interplay between the players of an RTS game, it is hypothesized that data sets of played games exhibit clustering properties as a result of the actions made by the players. LÄS MER