A Turing Test for Computer Game Bots

By Philip Hingston. 2009

166 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games (2016, methodology): “To provide the community with useful research environments, work was done towards building platforms based on videogames such as Torcs [27], Mario AI [20], Unreal’s BotPrize [10], the Atari Learning Environment [3], VizDoom [12], and Minecraft [11], all of which have allowed researchers to train dee”
StarCraft II: A New Challenge for Reinforcement Learning (2017, methodology): “The ALE is a prominent example in a rich tradition of video game benchmarks for AI [30], including Super Mario [35], Ms Pac-Man [26], Doom [13], Unreal Tournament [11], as well as general video game-playing frameworks [29, 5] and competitions [23].”
Programmation et apprentissage bayésien pour les jeux vidéo multi-joueurs, application à l'intelligence artificielle de jeux de stratégies temps-réel. (Bayesian Programming and Learning for Multi-Player Video Games, Application to RTS AI) (2012, background): “, 2011] combines learning from human traces with evolutionary learning for the structure of the artificial neural network, and leads to realistic (human-like) behaviors, in the context of the BotPrize challenge (judged by humans) [Hingston, 2009].”
Measuring Intelligence through Games (2011, methodology): “…agents that play particular games well; the very diverse collection of games used includes Othello, Go, Pac-Man, Super Mario Bros, Unreal Tournament and a car racing game (Loiacono et al. (2008, 2010); Togelius, Karakovskiy, and Baumgarten (2010); Togelius et al. (2008); Hingston (2009)).”
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning (2019, methodology): “This includes benchmarks based on Super Mario Bros (Karakovskiy and Togelius 2012), Minecraft (Johnson et al. 2016), Quake 3 (Hingston 2009; Beattie et al. 2016) and Pac-Man (Rohlf-shagen et al. 2018).”
Evaluation of StarCraft Artificial Intelligence Competition Bots by Experienced Human Players (2016, background): “In a First-person shooting study, additional human players were used as opponents to confuse human evaluators to critique “human-likeness” [10].”
Survey on Chatbot Design Techniques in Speech Conversation Systems (2015, background): “The scale of nonhuman to human is from 1 to 4 and the judge must evaluate the Chatbot in this range [16].”