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Your search keyword '"Bernik, Andrija"' showing total 4 results
Start Over Author "Bernik, Andrija" Topic artificial intelligence Publication Year Range Last 10 years
4 results on '"Bernik, Andrija"'

2. An Active Game Bot Detection with Security Bots

Author

Tomičić, Igor, Peharda, Tomislav, and Bernik, Andrija

Subjects
computer games, artificial intelligence, AI, bots, security, ComputingMilieux_PERSONALCOMPUTING
Abstract

Cheating in computer games can cause legit players to feel deprived, lose their interest and in time leave the game - which further leads to the decrease of profit in the gaming industry. Avoiding such a scenario is the main focus of research papers dealing with game cheating and cheat detection mechanisms within computer games, where there is a seemingly simple question to answer - is this player a legit human player, or an artificial bot? Most of the research is trying to identify patterns that could give away the presence of the bot, and in the same time, research is being done on building the bots with human-like behaviours which might deceive such mechanisms. In this paper, we present an active bot detection method implemented within a "security bot" - an artificial player which is infiltrated within the game and is able to identify suspicious game players, approach them, and perform a form of the Turing test on them, in order to identify possible cheating bots which have successfully deceived all the existing passive detection methods.

Published
2021

3. Anticheat System Based on Reinforcement Learning Agents in Unity.

Author

Lukas, Mihael, Tomicic, Igor, and Bernik, Andrija

Subjects
REINFORCEMENT learning, MULTIPLAYER games, CONCORD, MACHINE learning, BOTNETS, TEST systems
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2022
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4. An Active Game Bot Detection with Security Bots.

Author

Tomčić, Igor, Peharda, Tomislav, and Bernik, Andrija

Subjects
VIDEO games, ARTIFICIAL intelligence, CHEATING (Games)
Abstract

Cheating in computer games can cause legit players to feel deprived, lose their interest and in time leave the game - which further leads to the decrease of profit in the gaming industry. Avoiding such a scenario is the main focus of research papers dealing with game cheating and cheat detection mechanisms within computer games, where there is a seemingly simple question to answer - is this player a legit human player, or an artificial bot? Most of the research is trying to identify patterns that could give away the presence of the bot, and in the same time, research is being done on building the bots with human-like behaviours which might deceive such mechanisms. In this paper, we present an active bot detection method implemented within a "security bot" - an artificial player which is infiltrated within the game and is able to identify suspicious game players, approach them, and perform a form of the Turing test on them, in order to identify possible cheating bots which have successfully deceived all the existing passive detection methods. [ABSTRACT FROM AUTHOR]

Published
2021

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