Using deductive reasoning, the bot identifies friend or foe to ensure victory over humans in certain online games.
Rob Matheson | MIT News Office
MIT researchers have developed a bot equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret.
Many gaming bots have been built to keep up with human players. Earlier this year, a team from Carnegie Mellon University developed the world’s first bot that can beat professionals in multiplayer poker. DeepMind’s AlphaGo made headlines in 2016 for besting a professional Go player. Several bots have also been built to beat professional chess players or join forces in cooperative games such as online capture the flag. In these games, however, the bot knows its opponents and teammates from the start.
At the Conference on Neural Information Processing Systems next month, the researchers will present DeepRole, the first gaming bot that can win online multiplayer games in which the participants’ team allegiances are initially unclear. The bot is designed with novel “deductive reasoning” added into an AI algorithm commonly used for playing poker. This helps it reason about partially observable actions, to determine the probability that a given player is a teammate or opponent. In doing so, it quickly learns whom to ally with and which actions to take to ensure its team’s victory.
The researchers pitted DeepRole against human players in more than 4,000 rounds of the online game “The Resistance: Avalon.” In this game, players try to deduce their peers’ secret roles as the game progresses, while simultaneously hiding their own roles. As both a teammate and an opponent, DeepRole consistently outperformed human players.
“If you replace a human teammate with a bot, you can expect a higher win rate for your team. Bots are better partners,” says first author Jack Serrino ’18, who majored in electrical engineering and computer science at MIT and is an avid online “Avalon” player.
The work is part of a broader project to better model how humans make socially informed decisions. Doing so could help build robots that better understand, learn from, and work with humans.
“Humans learn from and cooperate with others, and that enables us to achieve together things that none of us can achieve alone,” says co-author Max Kleiman-Weiner, a postdoc in the Center for Brains, Minds and Machines and the Department of Brain and Cognitive Sciences at MIT, and at Harvard University. “Games like ‘Avalon’ better mimic the dynamic social settings humans experience in everyday life. You have to figure out who’s on your team and will work with you, whether it’s your first day of kindergarten or another day in your office.”
Joining Serrino and Kleiman-Weiner on the paper are David C. Parkes of Harvard and Joshua B. Tenenbaum, a professor of computational cognitive science and a member of MIT’s Computer Science and Artificial Intelligence Laboratory and the Center for Brains, Minds and Machines...
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