Efficient Deep Learning Bot Detection in Games Using Time Windows and Long Short-Term Memory (LSTM)
Open Access
- 26 October 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 195763-195771
Abstract
Bots in video games has been gaining the interest of industry as well as academia as a problem that has been enabled by the recent advances in deep learning and reinforcement learning. In turn several studies have attempted to establish bot detectors in various video games. In this article, we introduce a bot detection model that can implemented in real-time and provide feedback on whether a player that is being observed is a bot or human. The model uses a limited feature set and amount of time of observation in order to be small and generalize easily to other domains. We trained and tested our model in a series of replays for Starcraft: Brood War and have yielded a higher accuracy than past studies and a fraction of detection time.Keywords
This publication has 12 references indexed in Scilit:
- NGUARD2018
- StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer LearningIEEE Transactions on Emerging Topics in Computational Intelligence, 2018
- A time series classification approach to game bot detection2017
- Early Prediction of the Winner in StarCraft Matches2017
- Multimodal game bot detection using user behavioral characteristicsSpringerPlus, 2016
- Multiscale Bayesian Modeling for RTS Games: An Application to StarCraft AIIEEE Transactions on Computational Intelligence and AI in Games, 2015
- A Survey of Real-Time Strategy Game AI Research and Competition in StarCraftIEEE Transactions on Computational Intelligence and AI in Games, 2013
- Imitative learning for real-time strategy games2012
- Game Bot Detection via Avatar Trajectory AnalysisIEEE Transactions on Computational Intelligence and AI in Games, 2010
- Game bot identification based on manifold learning2008