| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document Help

Title:Evolucijski algoritmi za učenje agenta umetne inteligence pri igranju splošnih videoiger : magistrsko delo
Authors:ID Vöröš, Matjaž (Author)
ID Zamuda, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Voros_Matjaz_2019.pdf (11,19 MB)
MD5: E490FAE2030C54D626680D9C178594E9
PID: 20.500.12556/dkum/d3588644-46a9-4a7d-ab67-ea9af8ac1afb
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Videoigre so elektronske igre, ki z uporabnikovo pomočjo na zaslonu pokažejo vizualno povratno informacijo izbranih potez. Njihov osnovni namen je zabava in krajšanje časa. V zadnjih petih letih se je z mednarodnim tekovanjem inteligentnih agentov za igranje iger (angl. General Video Game AI competition; v nadaljevanju GVGAI) začelo novo poglavje. Tekmovanje GVGAI od udeležencev zahteva stvaritev agenta, ki s pomočjo optimizacijskih algoritmov poskuša doseči najboljši možen rezultat. Ker se nam je tekmovanje GVGAI zdelo zelo zanimivo, smo se odločili ustvariti agenta, ki s pomočjo evolucijskih algoritmov pri igranju videoiger, doseže kar se da dober rezultat. Agenta smo zasnovali po pregledu obstoječih optimizacijskih algoritmov. Za razliko od ostalih agentov, naš agent uporablja diferencialno evolucijo, ki še ni bila prikazana na tekmovanjih GVGAI. Dobljene rezultate primerjamo s pomočjo primerjalnega preizkusa GVGAI, vidimo pa da je naš agent statistično signifikantno boljši od večine, a obstaja prostor za napredek.
Keywords:evolucijski algoritem, videoigre, optimizacija, agent, igranje splošnih videoiger
Place of publishing:Maribor
Place of performance:Maribor
Publisher:M. Vöröš
Year of publishing:2019
Number of pages:VII, 75 str.
PID:20.500.12556/DKUM-73661 New window
UDC:004.032.26(043.2)
COBISS.SI-ID:22515734 New window
NUK URN:URN:SI:UM:DK:BCMCUVEP
Publication date in DKUM:21.06.2019
Views:1923
Downloads:169
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share


Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

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:30.05.2019

Secondary language

Language:English
Title:Evolutionary algorithms for artificial intelligence agent learning in general video game playing
Abstract:Video games are electronic games that show us visual feedback on the screen, based on the actions selected by the user. Their basic purpose is fun and entretainment. In the last five years, a new chapter for video gaming has opened in the form of GVGAI competition. The competition challanges the contestant to implement an agent that can maximize the score of played video games with usage of modern optimization algorithms. To us, the idea seemed very intriguing, so we decided to implement an agent that relies on evolutionary algorithms and achieves the highes score possible. We designed our agent after reviewing the existing optimization algorithms. Our agent uses diferential evolution, which was not yet used in a GVGAI competition. Our results are compared using the GVGAI benchmark and as we can see from the results our agent is statistically significantly better than most of the existing ones, but there is still room for improvement.
Keywords:evolutionary algorithm, GVGAI, videogame, optimization, agent, general game playing


Comments

Leave comment

You must log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica