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How did AI beat eight world champions at bridge?


On March 16, French AI startup NukkAI claimed on Twitter that in the following week, they would host a competition where the research firm would beat eight Bridge world champions. Bridge, unlike Chess or Go, is a more complicated game that involves cooperation and even covert signalling between players. It isn't considered a game in which AI would improve upon a human's performance considerably. In Bridge, opponents aren't aware of the cards that each of them holds, while, in Chess, opponents can make their strategies after observing the other's move. So much so that co-founder of Microsoft and avid bridge player Bill Gates once said that Bridge would be one of the last games where the computer couldn't better the human.

AI beats top players at Bridge in two-day tournament


In brief AI algorithms crushed eight world champions playing the card game Bridge, marking another milestone in machine learning systems becoming better than humans at specific games. Top Bridge players were invited to play against NooK, AI software developed by French startup NuukAI, in a tournament over two days in Paris. They battled against one another across 80 rounds, and the machine won 67 sets, beating humans at a rate of 83 per cent, according to The Guardian. NooK is made up of a combination of modern deep learning and older rule-based programmes. NuukAI's co-founder Jean-Baptiste Fantun said the company had developed the software over five years, and its decisions are easier to understand compared to today's black box-like systems.

AI Wins Paris Bridge Competition: Future Tense for Human Beings?


The race between technology and human beings has been a recurring theme in science fiction for more than a century. With human civilization witnessing phenomenal and unforeseen growth in high tech in the last few decades, the experiments of technology competing with the human agency have become much sharper and more frequent. There is no prize for guessing that much of it is occurring due to the exponential growth of AI. Recently, an artificial intelligence machine won the Paris Bridge Competition over human players and now the future in the gaming industry is dicey for humans. A very recent addition to such a stream of experimental exercises was found in Paris.

A Hybrid AI Just Beat Eight World Champions at Bridge--and Explained How It Did It


Champion bridge player Sharon Osberg once wrote, "Playing bridge is like running a business. While it's little surprise chess fell to number-crunching supercomputers long ago, you'd expect humans to maintain a more unassailable advantage in bridge, a game of incomplete information, cooperation, and sly communication. Over millennia, our brains have evolved to read subtle facial queues and body language. We've assembled sprawling societies dependent on the competition and cooperation of millions. Surely such skills are beyond the reach of machines? In recent years, the most advanced AI has begun encroaching on some of our most proudly held territory; the ability to navigate an uncertain world where information is limited, the game is infinitely nuanced, and no one succeeds alone. Last week, French startup NukkAI took another step when its NooK bridge-playing AI outplayed eight bridge world champions in a competition held in Paris. The game was simplified, and NooK didn't exactly go ...

A next-gen AI has managed to beat several bridge world champions


Follow us on Instagram and subscribe to our Telegram channel for the latest updates. PARIS, April 2 -- Several world champion bridge players had to accept defeat at the hands of an artificial intelligence system. A feat never previously achieved. The victories mark an important step in the development of AI, because of its use of'white box' AI, which acquires skills in a more human way, necessary to win at bridge compared to other strategy games such as chess. Until now, to demonstrate the potential of artificial intelligence, humans were pitted against machines.

As artificial intelligence gets smarter, is it game over for humans? Letters

The Guardian

You are right to acknowledge the work of Donald Michie (full disclosure: I'm his son) on artificial intelligence developing new insights rather than relying on brute force, and on the importance of AI communicating these insights to humans (The Guardian view on bridging human and machine learning: it's all in the game, 30 March). This pioneering work is important for the reasons you explain; it also speaks to debates on whether the rise of the robots will result in them enslaving us. My father argued that it was vital that the robots and AI of the future must be required (programmed) to explain what they were doing and why in terms understandable to humans. Without that, we really will be in trouble – from the routine (why did the driverless car crash?) to the existential. Your editorial was interesting, but NooK, the AI system it discussed, did not play bridge.

Artificial intelligence beats EIGHT world champion bridge players at their own game

Daily Mail - Science & tech

Bill Gates famously described bridge as'one of the last games in which the computer is not better'. But the Microsoft co-founder will be eating his words this week, following the news that an artificial intelligence bot has managed to beat not just one, but eight world champion bridge players at the game. French startup NukkAI spent four years developing the AI bot, called NooK, which took home the crown at the two-day Nukkai Challenge in Paris last week. In bridge, each of the four players, split into two teams, receives 13 cards in a hand. While other AI systems are typically trained by playing billions of rounds of a game, NooK was trained using a hybrid approach.

Knowledge-Based Paranoia Search in Trick-Taking Artificial Intelligence

This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find forced wins during trick-taking in the card game Skat; for some one of the most interesting card games for three players. It combines efficient partial information game-tree search with knowledge representation and reasoning. This worst-case analysis, initiated after a small number of tricks, leads to a prioritized choice of cards. We provide variants of KBPS for the declarer and the opponents, and an approximation to find a forced win against most worlds in the belief space. Replaying thousands of expert games, our evaluation indicates that the AIs with the new algorithms perform better than humans in their play, achieving an average score of over 1,000 points in the agreed standard for evaluating Skat tournaments, the extended Seeger system.

On the Power of Refined Skat Selection Artificial Intelligence

Skat is a fascinating combinatorial card game, show-casing many of the intrinsic challenges for modern AI systems such as cooperative and adversarial behaviors (among the players), randomness (in the deal), and partial knowledge (due to hidden cards). Given the larger number of tricks and higher degree of uncertainty, reinforcement learning is less effective compared to classical board games like Chess and Go. As within the game of Bridge, in Skat we have a bidding and trick-taking stage. Prior to the trick-taking and as part of the bidding process, one phase in the game is to select two skat cards, whose quality may influence subsequent playing performance drastically. This paper looks into different skat selection strategies. Besides predicting the probability of winning and other hand strength functions we propose hard expert-rules and a scoring functions based on refined skat evaluation features. Experiments emphasize the impact of the refined skat putting algorithm on the playing performance of the bots, especially for AI bidding and AI game selection.

ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning Artificial Intelligence

People have made remarkable progress in game AIs, especially in domain of perfect information game. However, trick-taking poker game, as a popular form of imperfect information game, has been regarded as a challenge for a long time. Since trick-taking game requires high level of not only reasoning, but also inference to excel, it can be a new milestone for imperfect information game AI. We study Gongzhu, a trick-taking game analogous to, but slightly simpler than contract bridge. Nonetheless, the strategies of Gongzhu are complex enough for both human and computer players. We train a strong Gongzhu AI ScrofaZero from \textit{tabula rasa} by deep reinforcement learning, while few previous efforts on solving trick-taking poker game utilize the representation power of neural networks. Also, we introduce new techniques for imperfect information game including stratified sampling, importance weighting, integral over equivalent class, Bayesian inference, etc. Our AI can achieve human expert level performance. The methodologies in building our program can be easily transferred into a wide range of trick-taking games.