Sökning: "Candy Crush Saga"

Visar resultat 1 - 5 av 10 uppsatser innehållade orden Candy Crush Saga.

1. 1. Dark Patterns : Den sura sidan av Candy Crush Saga

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Tilde Fredell; Matilda Haneling; [2023]
   Nyckelord :dark patterns; deceptive patterns; Candy Crush Saga; casual games; mobilspel; användargränssnitt;

   Sammanfattning : Many mobile games use a user interface designed to get their players to spend more time, money or social engagement within their application. This is done by deliberately misleading or otherwise confusing the user by, for example, making the player lose track of time when playing or by giving rewards to players who spend money or invite their friends into the game. LÄS MER

3. 2. Improving Generalization in Reinforcement Learningusing Skill-based Rewards

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Francesco Vito Lorenzo; [2020]
   Nyckelord :;

   Sammanfattning : Reinforcement Learning is a promising approach to develop intelligent agents that can help game developers in testing new content. However, applying it to a game with stochastic transitions like Candy Crush Friends Saga (CCFS) presents some challenges. LÄS MER

4. 3. How dark patterns affect desirability in Candy Crush Saga

   Kandidat-uppsats, Jönköping University/JTH, Datateknik och informatik

   Författare :Elin Söderholm; Sofia Flankkumäki; [2020]
   Nyckelord :Informatics; Dark Patterns; User Experience; Desirability; Candy Crush Saga; Mobile Games;

   Sammanfattning : Dark game design patterns are features used by game creators to manipulate the player to make certain choices. These patterns can lead to unintentional player actions causing negative experiences. LÄS MER

5. 4. Scaling Reinforcement Learning Solutions For Game Playtesting

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mathias Törnqvist; [2020]
   Nyckelord :;

   Sammanfattning : Games are commonly used as playground for AI research, specifically in the field of Reinforcement Learning (RL). RL has shown promising results in developing intelligent agents to play a multitude of games. Previous work have explored how RL agents can be used in the process of playtesting in game development. LÄS MER

7. 5. DQN Tackling the Game of Candy Crush Friends Saga : A Reinforcement Learning Approach

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Alice Karnsund; [2019]
   Nyckelord :;

   Sammanfattning : This degree project presents a reinforcement learning (RL) approach called deep Q-network (DQN) for learning how to play the game Candy Crush Friends Saga (CCFS). The DQN algorithm is implemented together with three extensions, which in 2015 resulted in a new state-of-the-art on the Atari 2600 domain. LÄS MER