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Visar resultat 1 - 5 av 13 uppsatser som matchar ovanstående sökkriterier.

1. 1. Identifying, Analysing and Comparing Organisational Cultures in the Game Development Industry : A comparative case study on the two Blizzards from 1997-2005.

   Kandidat-uppsats, Uppsala universitet/Institutionen för speldesign

   Författare :Klito Lamaj; Ruilai Xue; [2023]
   Nyckelord :Blizzard Entertainment; Competing Values Framework; Diablo II; Organisational culture; Starcraft;

   Sammanfattning : Organisational culture is a long debated research field, one that is greatly influential in modern day workspace, possibly deeply affecting organisational performance. This thesis is a case study on Blizzard entertainment from 1997 to 2005, where Blizzard North and Blizzard South, two organisations, existed and worked on some of the company’s most influential games. LÄS MER

3. 2. Towards Combinatorial Assignment in a Euclidean Environmentwith many Agents : applied in StarCraft II

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Edvin Bergström; [2022]
   Nyckelord :Combinatorial Assignment Euclidean Environment StarCraft II Coalition Structure Simultaneous coalition structure generation and assignment;

   Sammanfattning : This thesis investigates coordinating units through simultaneous coalition structuregeneration and task assignment in a complex Euclidean environment. The environmentused is StarCraft II, and the problem modeled and solved in the game is the distribution ofcombat units over the game’s map. LÄS MER

4. 3. Learning Multi-Agent Combat Scenarios in StarCraft II with League Training : an exploration of advanced learning techniques on a smaller scale

   Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

   Författare :Teodor Ganestål; [2022]
   Nyckelord :;

   Sammanfattning : Google DeepMind trained their state-of-the-art StarCraft II agent AlphaStar using leaguetraining with massive computational power. In this thesis we explore league training onsmall-scale combat scenarios in StarCraft II, using limited computational resources, to an-swer whether this approach is suitable for smaller problems. LÄS MER

5. 4. 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

7. 5. Gate Recurrent Unit Neural Networks for Hearing Instruments

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Harshit Sharma; Pallavi Rajanna; [2020]
   Nyckelord :Keyword Spotting; GRU; Gated; Recurrent; Unint; RNN; Hearing Instruments; Hearing; Instruments.; Technology and Engineering;

   Sammanfattning : Gated Recurrent Unit (GRU) neural networks have gained popularity for applications such as keyword spotting, speech recognition and other artificial intelligence applications. Typically for most applications training and inference is performed on cloud servers, and the result are transferred to the power constrained device, e.g. LÄS MER