Years ago, while on vacation in the Northwest, my husband and I rented a room in the home of a middle-aged couple, one of whom had recently retired. The house was old, beautiful, and cozily laden with objects that signalled domestic inertia. It sat on a lush, wild sprawl of farmland that immediately inspired fantasies of leaving San Francisco and our tech jobs, foraging for mushrooms, administering to septic systems, and turning over soil. One morning over breakfast, conversation shifted to our host's retirement. He was glad to have more time at home with his wife and their dog.

Competitive CounterStrike: Global Offensive player Adam Bahriz will probably kill you in-game. He's so skilled that he landed a contract with Team Envy, an esports organization that's home to some of North America's highest-ranking competitive eSports players. Bahriz also just happens to be deaf and legally blind, with a condition known as HSAN 8. "What do you guys want to do? Just bust out A? I can buy smoke," Bahriz says. His teammates immediately jump in to mock him and shut him down.

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.

Researchers have developed an artificial intelligence-enabled machine that can beat human players in a tricky online multiplayer game where player roles and motives are kept secret, says a study. It was presented at International Conference on Information Systems. The machine, called'DeepRole', is the first gaming bot that can win online multiplayer games in which the participants' team allegiances are initially unclear, according the study from Massachusetts Institute of Technology (MIT), US. 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.

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.

While billionaires buy up property in New Zealand and pay technologists huge sums of money for advice on how to keep their staff in check after "the event" – that is, whatever it is that wipes out enough of the planet to justify living in bunkers – the rest of us are left to deal with the looming threat of catastrophe by playing video games. Bethesda Game Studios' Fallout series offers a very American take on the post-apocalypse: humans, ghouls and mutants protect their respective corners of the wasteland with big guns and power armour, in a retro future with sci-fi technology and a 1950s aesthetic. The games present a ravaged, irradiated all-American picket-fence fantasy with classic cars, suburban homes and US landmarks devastated by nuclear bombs. Fallouts 3 and 4 are explorative role-playing games that cast the player as a survivor emerging from a vault after more than 100 years into a world they don't recognise – though, after a few hours, they have significantly more weapons and resources than the average pitiable remnant of humanity. The games offer the player 100 or more hours exploring the wasteland and meeting its dogged inhabitants.

We present an innovative approach for large-scale data collection in human-robot interaction research through the use of online multi-player games. By casting a robotic task as a collaborative game, we gather thousands of examples of human-human interactions online, and then leverage this corpus of action and dialog data to create contextually relevant, social and task-oriented behaviors for human-robot interaction in the real world. We demonstrate our work in a collaborative search and retrieval task requiring dialog, action synchronization and action sequencing between the human and robot partners. A user study performed at the Boston Museum of Science shows that the autonomous robot exhibits many of the same patterns of behavior that were observed in the online dataset and survey results rate the robot similarly to human partners in several critical measures.