diplomsko delo
Edin Ćehić (Author), Bojan Klemenc (Mentor)

Abstract

V zadnjih letih smo priča izjemno hitremu razvoju umetne inteligence v splošni rabi. Specifično nas zanima uporaba umetne inteligence za ustvarjanje. Čeprav mnogi kritizirajo to smer, verjamemo, da je lahko zelo uporabna v prihodnosti, še posebej pri razvoju video iger. Umetna inteligenca, zlasti generativne nevronske mreže, se že uporabljajo za generiranje tekstur in grafik. V naši raziskavi pa smo se osredotočili na uporabo generativnih nasprotniških nevronskih mreže za ustvarjanje nivojev ali svetov v računalniških igrah, ki se dinamično prilagajajo uporabnikovim dejanjem. Uporabili smo odprtokodni klon igre Hill Climb Racing, zasnovan za učenje navidezno inteligentnih agentov. Stopnje smo generirali z našo nevronsko mrežo, jih vstavili v igro in nato prilagajali z uporabo nevronske mreže glede na uspešnost agentov pri reševanju nivojev.

Keywords

računalniške igre;generativne nevronske mreže;spodbujevano učenje;generativne nasprotniške mreže;visokošolski strokovni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [E. Ćehić]
UDC: 004.5:794:004.8(043.2)
COBISS: 212275715 Link will open in a new window
Views: 64
Downloads: 15
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: Dynamic level modification in a computer game using a generative adversarial neural network
Secondary abstract: In recent years, we have witnessed an extremely rapid development of artificial intelligence in general use. Specifically, we are interested in the application of artificial intelligence for generation of art and assets. Although many criticize this direction, we believe it can be very useful in the future, especially in video game development. Artificial intelligence, particularly generative neural networks, is already used for generating textures and graphics. In our research, however, we focused on using generative adversarial neural networks to create levels or worlds in computer games that dynamically adapt to the user's actions. We used an open-source clone of the game Hill Climb Racing, designed for training virtually intelligent agents. We generated levels with our generative neural network, integrated them into the game, and then modified the levels with the neural network based on the agents' performance in solving them.
Secondary keywords: computer games;generative neural networks;reinforcement learning;generative adversarial networks;computer science;diploma;Videoigre;Umetna inteligenca;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000470
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 1 spletni vir (1 datoteka PDF (49 str.))
ID: 25011544