Tackling generation of combat encounters in role-playing digital games

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440816" target="_blank" >RIV/00216208:11320/21:10440816 - isvavai.cz</a>

Výsledek na webu

<a href="http://ceur-ws.org/Vol-2962/paper34.pdf" target="_blank" >http://ceur-ws.org/Vol-2962/paper34.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Tackling generation of combat encounters in role-playing digital games

Popis výsledku v původním jazyce

Procedural content generation (PCG) has been used in digital games since the early 1980s. Here we focus on a new problem of generating personalized combat encounters in role playing video games (RPG). A game should provide a player with combat encounters of adequate difficulties, which ideally should be matching the player’s performance in order for a game to provide adequate challenge to the player. In this paper, we describe our own reinforcement learning algorithm that estimates difficulties of combat encounters during game runtime, which can be them used to find next suitable combat encounter of desired difficulty in a stochastic hill-climbing manner. After a player finishes the encounter, its result is propagated through the matrix to update the estimations of not only the presented combat encounter, but also similar ones. To test our solution, we conducted a preliminary study with human players on a simplified RPG game we have developed. The data collected suggests our algorithm can adapt the matrix to the player performance fast from little amounts of data, even though not precisely.

Název v anglickém jazyce

Tackling generation of combat encounters in role-playing digital games

Popis výsledku anglicky

Procedural content generation (PCG) has been used in digital games since the early 1980s. Here we focus on a new problem of generating personalized combat encounters in role playing video games (RPG). A game should provide a player with combat encounters of adequate difficulties, which ideally should be matching the player’s performance in order for a game to provide adequate challenge to the player. In this paper, we describe our own reinforcement learning algorithm that estimates difficulties of combat encounters during game runtime, which can be them used to find next suitable combat encounter of desired difficulty in a stochastic hill-climbing manner. After a player finishes the encounter, its result is propagated through the matrix to update the estimations of not only the presented combat encounter, but also similar ones. To test our solution, we conducted a preliminary study with human players on a simplified RPG game we have developed. The data collected suggests our algorithm can adapt the matrix to the player performance fast from little amounts of data, even though not precisely.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

CEUR Workshop Proceedings

ISBN

—

ISSN

1613-0073

e-ISSN

—

Počet stran výsledku

8

Strana od-do

69-76

Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Neuveden

Místo konání akce

Helpa, Slovakia

Datum konání akce

24. 9. 2021

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—