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Pregled bibliografske jedinice broj: 1187388

Anticheat System Based on Reinforcement Learning Agents in Unity


Lukaš, Mihael; Tomičić, Igor; Bernik, Andrija
Anticheat System Based on Reinforcement Learning Agents in Unity // Information, 13 (2022), 4; 173, 12 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1187388 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Anticheat System Based on Reinforcement Learning Agents in Unity

Autori
Lukaš, Mihael ; Tomičić, Igor ; Bernik, Andrija

Izvornik
Information (2078-2489) 13 (2022), 4; 173, 12

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
security ; artificial intelligence ; infosec ; reinforcement learning ; agents ; games ; gaming ; unity

Sažetak
Game cheating is a common occurrence that may degrade the experience of “honest” players. It can be hindered by using appropriate anticheat systems, which are being considered as a subset of security-related issues. In this paper, we implement and test an anticheat system whose main goal is to help differentiate human players from AI players. For this purpose, we first developed a multiplayer game inside game engine Unity that would serve as a framework for training the reinforcement learning agent. This agent would thus learn to differentiate human players from bots within the game. We implemented the Machine Learning Agents Toolkit library, which uses the proximal policy optimization algorithm. AI players are implemented using state machines, and perform certain actions depending on which condition is satisfied. Two experiments were carried out for testing the agent and showed promising results for identifying artificial players.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
UNIN--UNIN-DRUŠ-21-1-6 - Utjecaj senzorske mehanike, audio impulsa te dubinskih mapa na uživanje, pažnju i percepciju korisnika (nastavak predistraživanja i analiza rezultata) (SMAI-Z) (Bernik, Andrija, UNIN ) ( CroRIS)

Ustanove:
Fakultet organizacije i informatike, Varaždin,
Sveučilište Sjever, Koprivnica

Profili:

Avatar Url Igor Tomičić (autor)

Avatar Url Andrija Bernik (autor)

Poveznice na cjeloviti tekst rada:

www.mdpi.com

Citiraj ovu publikaciju:

Lukaš, Mihael; Tomičić, Igor; Bernik, Andrija
Anticheat System Based on Reinforcement Learning Agents in Unity // Information, 13 (2022), 4; 173, 12 (međunarodna recenzija, članak, znanstveni)
Lukaš, M., Tomičić, I. & Bernik, A. (2022) Anticheat System Based on Reinforcement Learning Agents in Unity. Information, 13 (4), 173, 12.
@article{article, author = {Luka\v{s}, Mihael and Tomi\v{c}i\'{c}, Igor and Bernik, Andrija}, year = {2022}, pages = {12}, chapter = {173}, keywords = {security, artificial intelligence, infosec, reinforcement learning, agents, games, gaming, unity}, journal = {Information}, volume = {13}, number = {4}, issn = {2078-2489}, title = {Anticheat System Based on Reinforcement Learning Agents in Unity}, keyword = {security, artificial intelligence, infosec, reinforcement learning, agents, games, gaming, unity}, chapternumber = {173} }
@article{article, author = {Luka\v{s}, Mihael and Tomi\v{c}i\'{c}, Igor and Bernik, Andrija}, year = {2022}, pages = {12}, chapter = {173}, keywords = {security, artificial intelligence, infosec, reinforcement learning, agents, games, gaming, unity}, journal = {Information}, volume = {13}, number = {4}, issn = {2078-2489}, title = {Anticheat System Based on Reinforcement Learning Agents in Unity}, keyword = {security, artificial intelligence, infosec, reinforcement learning, agents, games, gaming, unity}, chapternumber = {173} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus





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