Player profiling using hidden markov models supported with the sliding window method

Alper Kilic, Mehmet Akif Gunes, Sanem Sariel

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

1 Atıf (Scopus)

Özet

In this paper, we present a player profiling system applicable for both human players and bots in video games. The Vindinium artificial intelligence (AI) contest is selected as the test-bed for analyzing the performance of our system. In this game, AI bots compete with each other in a systematically generated environment to achieve the highest score. Our profiling method is based on Hidden Markov Model (HMM) constructed by using consecutive actions of AI bots and improved with the initial training phase and our sliding window approach. The method is evaluated for three different performance criteria: recognition of bots, grouping bots that have similar game styles and tracking changes in the strategy of a single bot through the game. The results indicate that the method is promising with 90,04% binary classification success in average.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence
EditörlerJaap van den Herik, Joaquim Filipe, Joaquim Filipe
YayınlayanSciTePress
Sayfalar362-369
Sayfa sayısı8
ISBN (Elektronik)9789897581724
DOI'lar
Yayın durumuYayınlandı - 2016
Etkinlik8th International Conference on Agents and Artificial Intelligence, ICAART 2016 - Rome, Italy
Süre: 24 Şub 201626 Şub 2016

Yayın serisi

AdıICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence
Hacim2

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???event.eventtypes.event.conference???8th International Conference on Agents and Artificial Intelligence, ICAART 2016
Ülke/BölgeItaly
ŞehirRome
Periyot24/02/1626/02/16

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Publisher Copyright:
Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

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