Essays about: "Game AI"
Showing result 21 - 25 of 126 essays containing the words Game AI.
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21. How does the performance of NEAT compare to Reinforcement Learning?
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : This study examined the relative performance of Deep Reinforcement Learning compared to a neuroevolution algorithm called NEAT when used to train AIs in a discrete game environment. Today there are many AI techniques to choose from among which NEAT and RL have become popular alternatives. READ MORE
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22. Embracing AWKWARD! A Hybrid Architecture for Adjustable Socially-Aware Agents
University essay from Umeå universitet/Institutionen för datavetenskapAbstract : This dissertation presents AWKWARD: a hybrid architecture for the development of socially aware agents in Multi-Agent Systems (MAS). AWKWARD bridges Artificial Intelligence (AI) methods for their individual and combined strengths; Behaviour Oriented Design (BOD) is used to develop reactive planning agents, the OperA framework is used to modeland validate agent behaviour as per social norms, and Reinforcement Learning (RL) is used to optimise plan structures that induce desirable social outcomes. READ MORE
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23. AI as a Creative Colleague in Game Level Design
University essay from Malmö universitet/Fakulteten för teknik och samhälle (TS)Abstract : This paper describes a modification to the mixed-initiative co-creative tool, the Evolutionary Dungeon Designer, and a study performed to evaluate and analyze the co-creative relationship between human designer and AI. The relationship between AI and human designer in creative processes is delicate, as adjusting the autonomy of the AI can negatively affect the user experience. READ MORE
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24. Classifying football players using cluster analysis
University essay from Uppsala universitet/Statistik, AI och data scienceAbstract : This thesis attempts to apply cluster analysis to the game of football wherea set of football players were clustered into subsets based on data gatheredover a season. Two different types of clustering algorithms were used and theresults from both these methods were then looked at discussed. READ MORE
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25. Learning Multi-Agent Combat Scenarios in StarCraft II with League Training : an exploration of advanced learning techniques on a smaller scale
University essay from Linköpings universitet/Artificiell intelligens och integrerade datorsystemAbstract : 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. READ MORE
