Bootstrapping from Game Tree Search

Neural Information Processing Systems

In this paper we introduce a new algorithm for updating the parameters of a heuristic evaluation function, by updating the heuristic towards the values computed by an alpha-beta search. Our algorithm differs from previous approaches to learning from search, such as Samuels checkers player and the TD-Leaf algorithm, in two key ways. First, we update all nodes in the search tree, rather than a single node. Second, we use the outcome of a deep search, instead of the outcome of a subsequent search, as the training signal for the evaluation function. We implemented our algorithm in a chess program Meep, using a linear heuristic function.

Iterative ranking from pair-wise comparisons

Neural Information Processing Systems

The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining ranking, finding'scores' for each object (e.g. In this paper, we propose a novel iterative rank aggregation algorithm for discovering scores for objects from pairwise comparisons. The algorithm has a natural random walk interpretation over the graph of objects with edges present between two objects if they are compared; the scores turn out to be the stationary probability of this random walk.

What is the difference between supervised and unsupervised machine learning?


This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we use daily use machine learning algorithms, including AI assistants, web search and machine translation. Your social media news feed is powered by a machine learning algorithm. The recommended videos you see on YouTube and Netflix are the result of a machine learning model.

ZugZwang Academy Chess Classes in Bangalore Chess Education & Coaching


Is today's schooling preparing your child to be a creator? Fluid Intelligence is the ability to solve problems one has never faced before. We believe this is the single most important ability that will make a huge difference in the life of any child. The most important thinking skills such as decision making, problem solving and logical reasoning is what helps build fluid intelligence. We are specialists in working with children from a very young age to develop their fluid intelligence for a life long and ever lasting impact.

How Artificial Intelligence Could Help Video Gamers Create the Exact Games They Want to Play

TIME - Tech

For video game fans, the concept of artificial intelligence (AI) is just as familiar as extra lives, respawns, and end bosses. Gamers have spent decades going up against computer-controlled opponents, whether a Pong paddle trying to prevent them from scoring a point or Bowser trying to stop Mario from rescuing Princess Peach. But recent developments in AI are pushing the gaming field even further, as researchers develop algorithms that can help fans make exciting new titles on their own. The history of AI and that of gaming are inexorably intertwined. Early AI researchers saw games like chess as markers of intelligence, and thus perfect testing grounds for their work.

Why are Machine Learning Projects so Hard to Manage? - KDnuggets


I've watched lots of companies attempt to deploy machine learning -- some succeed wildly, and some fail spectacularly. One constant is that machine learning teams have a hard time setting goals and setting expectations. Is it harder to beat Kasparov at chess or pick up and physically move the chess pieces? Computers beat the world champion chess player over twenty years ago, but reliably grasping and lifting objects is still an unsolved research problem. Humans are not good at evaluating what will be hard for AI and what will be easy.

Explained: The Artificial Intelligence Race is an Arms Race


Most chess computers play a purely mathematical strategy in a game yet to be solved. They are raw calculators and look like it too. AlphaZero, at least in style, appears to play every bit like a human. It makes long-term positional plays as if it can visualize the board; spectacular piece sacrifices that no computer could ever possibly pull off, and exploitative exchanges that would make a computer, if it were able, cringe with complexity. In short, AlphaZero is a genuine intelligence.

8 Incredible Examples of Artificial Intelligence in Action


Artificial Intelligence (AI) is not just a futuristic concept that might evolutionize the world as we know it, but rather an already-existing technology that strongly impacts various industries. Although we are still far from stretching AI to the fullest extent, smart systems are already changing the way we live and do business in many different areas. Experts define AI as the simulation of human intelligence processes by machines, especially computer systems. So, what can intelligent machines do for us? We prepared a list of seven incredible examples of AI in action.

Polygames: Improved Zero Learning Machine Learning

Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games. It can be improved using a fully convolutional structure (no fully connected layer). Using such an architecture plus global pooling, we can create bots independent of the board size. The training can be made more robust by keeping track of the best checkpoints during the training and by training against them. Using these features, we release Polygames, our framework for Zero learning, with its library of games and its checkpoints. We won against strong humans at the game of Hex in 19x19, which was often said to be untractable for zero learning; and in Havannah. We also won several first places at the TAAI competitions.

Regulation will 'stifle' AI and hand the lead to Russia and China, warns Garry Kasparov


Garry Kasparov has warned that any attempts by the Government to regulate artificial intelligence (AI) could "stifle" its development and give Russia and China an advantage. The former world chess champion has become an advocate for AI development following his resignation from professional chess in 2005. He told The Telegraph that "the government should be involved" in helping researchers and private firms to develop AI in order to "pave the road" for the technology. However, he cautioned against governments attempting to regulate the technology too closely. "It's too early for the government to interfere," he said.