Master’s dissertation (Dissertations and theses)
Learning Artificial Intelligence in Large-Scale Video Games: A First Case Study with Hearthstone: Heroes of Warcraft
Taralla, David
2015
 

Files


Full Text
tfe-dtaralla.pdf
Publisher postprint (10.55 MB)
Download
Annexes
tfe-slides-dtaralla.pdf
Publisher postprint (4.37 MB)
Supporting slides for the master's thesis oral defense
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
artificial intelligence; video games; hearthstone; supervised learning; extremely randomized trees; extratrees
Abstract :
[en] Over the past twenty years, video games have become more and more complex thanks to the emergence of new computing technologies. The challenges players face now involve the simultaneous consideration of many game environment variables — they usually wander in rich 3D environments and have the choice to take numerous actions at any time, and taking an action has combinatorial consequences. However, the artificial intelligence (AI) featured in those games is often not complex enough to feel natural (human). Today's AI is still most of the time hard-coded, but as the game environments become increasingly complex, this task becomes exponentially difficult. To circumvent this issue and come with rich autonomous agents in large-scale video games, many research works already tried and succeeded in making video game AI learn instead of being taught. This thesis does its bit towards this goal. In this work, supervised learning classification based on extremely randomized trees is attempted as a solution to the problem of selecting an action amongst the set of available ones in a given state. In particular, we place ourselves in the context where no assumptions are made on the kind of actions available and where action simulations are not possible to find out what consequences these have on the game. This approach is tested on the collectible card game Hearthstone: HoW, for which an easily-extensible simulator was built. Encouraging results were obtained when facing Nora, the resulting Mage agent, against random and scripted (medium-level) Mage players. Furthermore, besides quantitative results, a qualitative experiment showed that the agent successfully learned to exhibit a board control behavior without having been explicitly taught to do so.
Disciplines :
Computer science
Author, co-author :
Taralla, David ;  Université de Liège - ULiège > 2e an. master ingé. civ. info., fin. appr.
Language :
English
Title :
Learning Artificial Intelligence in Large-Scale Video Games: A First Case Study with Hearthstone: Heroes of Warcraft
Alternative titles :
[fr] Apprentissage d'une intelligence artificielle appliquée aux jeux vidéo à grande échelle : Une première étude avec Hearthstone: Heroes of Warcraft
Defense date :
23 June 2015
Number of pages :
67
Institution :
ULiège - Université de Liège
Degree :
MSc in Computer Science & Engineering
Promotor :
Ernst, Damien  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
President :
Boigelot, Bernard  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Jury member :
Fonteneau, Raphaël ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Geurts, Pierre ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Wehenkel, Louis  ;  Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science
Available on ORBi :
since 26 June 2015

Statistics


Number of views
903 (31 by ULiège)
Number of downloads
1266 (25 by ULiège)

Bibliography


Similar publications



Contact ORBi