Vis enkel innførsel

dc.contributor.advisorHaddow, Paulinenb_NO
dc.contributor.authorRathe, Espen Aurannb_NO
dc.contributor.authorSvendsen, Jørgen Bøenb_NO
dc.date.accessioned2014-12-19T13:39:08Z
dc.date.available2014-12-19T13:39:08Z
dc.date.created2012-11-08nb_NO
dc.date.issued2012nb_NO
dc.identifier566284nb_NO
dc.identifierntnudaim:6854nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/252993
dc.description.abstractThis thesis presents an approach to controlling Micromanagement in Real-Time Strategy (RTS) computer games using Potential Fields (PF) that are tuned with Multi-Objectve Optimized Evolutionary Algorithms (MOEA), specifically the Nondominated Sorting Genetic Algorithm (NSGA-II). The classic RTS title textit{StarCraft: Broodwar} has been chosen as testing platform due to its status in the competitive AI scene, the amount of detailed information available from previous research and projects, and the free open-source framework Brood War Application Programming Interface (BWAPI). The proposed AI controls its units by placing several types of Potential Fields onto the battlefield. The weights behind the PFs' calculations are optimized using NSGA-II. This work is an attempt to improve on previous methods done with PF in RTS. The results indicate that Multi-Objective Optimization is a suited method for optimizing Potential Fields in RTS games.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaim:6854no_NO
dc.subjectMTDT datateknikkno_NO
dc.subjectIntelligente systemerno_NO
dc.titleMicromanagement in StarCraft using Potential Fields tuned with a Multi- Objective Genetic Algorithmnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber84nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


Tilhørende fil(er)

Thumbnail
Thumbnail
Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel