Kriesgpiel, or partially observable chess, is appealing to the AI community due to its similarity to real-world applications in which a decision maker is not a lone agent changing the environment. This paper applies the framework of Interactive POMDPs to design a competent Kriegspiel player. The novel element, compared to the existing approaches, is to model the opponent as a competent player and to predict his likely moves. The moves of our own player can then be computed based on these predictions. The problem is challenging because, first, there are many possible world states the agent has to keep track of.
One video, for me, changed everything. It's footage from the old Atari game Breakout, the one where you slide a paddle left and right along the bottom of the screen, trying to destroy bricks by bouncing a ball into them. You may have read about the player of the game: an algorithm developed by DeepMind, the British artificial intelligence company whose AlphaGo programme also beat one of the greatest ever Go players, Lee Sedol, earlier this year. Perhaps you expect a computer to be good at computer games? Once they know what to do, they certainly do it faster and more consistently than any human. DeepMind's Breakout player knew nothing, however. It was not programmed with instructions on how the game works; it wasn't even told how to use the controls. All it had was the image on the screen and the command to try to get as many points as possible. At first, the paddle lets the ball drop into oblivion, knowing no better. Eventually, just mucking about, it knocks the ball back, destroys a brick and gets a point, so it recognises this and does it more often.
Nguyen, Truong-Huy Dinh (National University of Singapore) | Hsu, David (National University of Singapore) | Lee, Wee-Sun (National University of Singapore) | Leong, Tze-Yun (National University of Singapore) | Kaelbling, Leslie Pack (Massachusetts Institute of Technology) | Lozano-Perez, Tomas (Massachusetts Institute of Technology) | Grant, Andrew Haydn (Singapore-MIT GAMBIT Game Lab)
We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.
Machines are finally getting the best of humans at poker. Two artificial intelligence (AI) programs have finally proven they "know when to hold'em, and when to fold'em," recently beating human professional card players for the first time at the popular poker game of Texas Hold'em. And this week the team behind one of those AIs, known as DeepStack, has divulged some of the secrets to its success--a triumph that could one day lead to AIs that perform tasks ranging from from beefing up airline security to simplifying business negotiations. AIs have long dominated games such as chess, and last year one conquered Go, but they have made relatively lousy poker players. In DeepStack researchers have broken their poker losing streak by combining new algorithms and deep machine learning, a form of computer science that in some ways mimics the human brain, allowing machines to teach themselves.
DeepMind Technologies is a British artificial intelligence company founded in September 2010, currently owned by Alphabet Inc.. The company is based in London, but has research centres in California, Canada, and France. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans, as well as a Neural Turing machine, or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain. The company made headlines in 2016 after its AlphaGo program beat a human professional Go player for the first time in October 2015 and again when AlphaGo beat Lee Sedol, the world champion, in a five-game match, which was the subject of a documentary film. A more generic program, AlphaZero, beat the most powerful programs playing go, chess and shogi (Japanese chess) after a few hours of play against itself using reinforcement learning.