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Title:Učenje agentov in primerjava obstoječih rešitev za igro MicroRTS na platformi GIANT : magistrsko delo
Authors:ID Omerzu, Anže (Author)
ID Ravber, Miha (Mentor) More about this mentor... New window
ID Šmid, Marko (Comentor)
Files:.pdf MAG_Omerzu_Anze_2026.pdf (2,77 MB)
MD5: 92D82A77271AD806F67A182EE098A271
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo obravnava razvoj in vrednotenje inteligentnih agentov za realnočasovno strateško igro MicroRTS, ki predstavlja poenostavljeno, a raziskovalno bogato okolje za testiranje metod umetne inteligence. Igro smo implementirali v pogonu Unity in jo integrirali v platformo GIANT, kar omogoča modularno izvajanje agentov, ponovljivo eksperimentiranje ter medsebojno primerjavo strategij. Glavni cilj naloge je bil razviti štiri nove agente, zasnovane z vedenjskimi drevesi, ter jih primerjati z izbranimi obstoječimi rešitvami. Učinkovitost agentov smo testirali na različnih mapah MicroRTS in jih ocenili z rangirnim sistemom TrueSkill. Rezultati kažejo, da vedenjska drevesa omogočajo pregledno, modularno in učinkovito zasnovo agentov, ki dosežejo konkurenčne rezultate v primerjavi z referenčnimi rešitvami. Integrirano okolje MicroRTS predstavlja uporabno raziskovalno osnovo za nadaljnji razvoj agentov in širitev eksperimentalnih scenarijev. Kljub omejitvam poenostavljenega okolja naloga ponuja celovit prispevek k razvoju razložljivih inteligentnih agentov v realnočasovnih strateških igrah.
Keywords:MicroRTS, Unity, platforma GIANT, vedenjska drevesa, inteligentni agenti, TrueSkill
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[A. Omerzu]
Year of publishing:2026
Number of pages:1 spletni vir (1 datoteka PDF (IX, 50 str.))
PID:20.500.12556/DKUM-97650 New window
UDC:004.388.4:004.8(043.2)
COBISS.SI-ID:281709571 New window
Publication date in DKUM:29.05.2026
Views:152
Downloads:10
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:29.03.2026

Secondary language

Language:English
Title:Agent training and comparison of existing solutions for the MicroRTS game on the GIANT platform
Abstract:This master's thesis addresses the development and evaluation of intelligent agents for the real-time strategy game MicroRTS, which represents a simplified yet sufficiently rich research environment for testing artificial intelligence methods. The game was reimplemented in the Unity engine and integrated into the GIANT platform, enabling modular agent execution, reproducible experimentation, and comparison of strategies. The main objective of the thesis was to develop four new agents based on behavior trees and compare them with selected existing solutions. The agents’ performance was tested on different MicroRTS maps and evaluated using the TrueSkill rating system. The results show that behavior trees enable a transparent, modular, and effective design of agents that achieve competitive results compared to reference solutions. The integrated MicroRTS environment represents a useful research basis for further agent development and the expansion of experimental scenarios. Despite the limitations of the simplified environment, the thesis offers a comprehensive contribution to the development of explainable intelligent agents in real-time strategy games.
Keywords:MicroRTS, Unity, GIANT platform, behavior trees, intelligent agents, TrueSkill


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