Sökning: "Wargame"
Visar resultat 1 - 5 av 7 uppsatser innehållade ordet Wargame.
1. Användarbehov för sannolikhetsberäknande appar för användning under en match i krigsspel
Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : I den här studien så utvecklades en app som kan räkna ut och visa sannolikhetsfördelningen av skada som enheter gör i Warhammer 40k. Syftet var att appen skulle fungera som ett hjälpmedel för spelare där den ger information som är viktig för många av valen man gör i spelet. LÄS MER
2. Exploring Game Balance and Tactics with AI in the Educational Wargame Counter-Air
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This report describes a study of the game balance of the wargame Counter-Air,using the artificial intelligence model AlphaZero. The project also analyzed the most effectivestrategies in the game regarding allocation of pieces to a set number of roles. Counter-Air is a board game being developed for use in tactical training of military officers. LÄS MER
3. Comparison of ’Fog of War’ models in digital wargames : Using Entity-Component-System architecture and ArcGIS
M1-uppsats, KTH/Hälsoinformatik och logistikSammanfattning : Fog of War is a term for uncertainty in situational awareness. Fog of War is an essential part of a wargame which causes the participating units’ perception of the environment to be distorted and altered. Introducing a certain amount of uncertainty helps to better mimic the situation on the battlefield. LÄS MER
4. Aerial Combat in the Educational Wargame Counter Air With AlphaZero
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This report investigates the game balance of Philip Sabin's educational wargameCounter Air using AlphaZero and MCTS. The results show that the attacking side has theadvantage and finds a better strategy faster than the defender. LÄS MER
5. AI for an Imperfect-Information Wargame with Self-Play Reinforcement Learning
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : The task of training AIs for imperfect-information games has long been difficult. However, recently the algorithm ReBeL, a general framework for self-play reinforcement learning, has been shown to excel at heads-up no-limit Texas hold 'em, among other imperfect-information games. LÄS MER