An Artificial Player for a Turn-based Strategy Game

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013523" target="_blank" >RIV/62690094:18450/17:50013523 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-54472-4_43" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-54472-4_43</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-54472-4_43" target="_blank" >10.1007/978-3-319-54472-4_43</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Artificial Player for a Turn-based Strategy Game

Popis výsledku v původním jazyce

This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state space, are tested. In the first phase, common methods such as heuristics, influence maps, and decision trees are used. While they have many advantages (speed, simplicity and the ability to find a solution in a reasonable time), they provide rather average results. In the second phase, the player is enhanced by an evolutionary algorithm. The algorithm adjusts several parameters of the player that were originally determined empirically. In the third phase, a learning process based on recorded moves from previous games played is used. The results show that incorporating evolutionary algorithms can significantly improve the efficiency of the artificial player without necessarily increasing the processing time.

Název v anglickém jazyce

An Artificial Player for a Turn-based Strategy Game

Popis výsledku anglicky

This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state space, are tested. In the first phase, common methods such as heuristics, influence maps, and decision trees are used. While they have many advantages (speed, simplicity and the ability to find a solution in a reasonable time), they provide rather average results. In the second phase, the player is enhanced by an evolutionary algorithm. The algorithm adjusts several parameters of the player that were originally determined empirically. In the third phase, a learning process based on recorded moves from previous games played is used. The results show that incorporating evolutionary algorithms can significantly improve the efficiency of the artificial player without necessarily increasing the processing time.

Druh

D - Stať ve sborníku

CEP obor

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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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Intelligent Information and Database Systems

ISBN

978-3-319-54471-7

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

455-465

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Kanazawa

Datum konání akce

3. 4. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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