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Gerald Tesauro
Gerald J. "Gerry" Tesauro is an American computer scientist and a researcher at IBM, known for his development of TD-Gammon, a backgammon program that taught itself to play at a world-championship level through self-play and temporal difference learning, an early success in reinforcement learning and neural networks. He subsequently researched on autonomic computing, multi-agent systems for e-commerce, and contributed to the game strategy algorithms for IBM Watson. Career Education Tesauro earned a B.S. in physics from the University of Maryland, College Park. He then pursued graduate studies in plasma physics at Princeton University, supported by a Hertz Foundation Fellowship starting in 1980. He completed his Ph.D. in theoretical physics in 1986 under the supervision of Nobel laureate Philip W. Anderson. Backgammon After completing his Ph.D., he undertook postdoctoral research at the Center for Complex Systems Research, University of Illinois at Urbana-Champaign. During ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
TD-Gammon
TD-Gammon is a computer backgammon program developed in the 1990s by Gerald Tesauro at IBM's Thomas J. Watson Research Center. Its name comes from the fact that it is an artificial neural net trained by a form of temporal-difference learning, specifically temporal-difference learning#TD-Lambda, TD-Lambda. It explored strategies that humans had not pursued and led to advances in the theory of correct backgammon play. In 1993, TD-Gammon (version 2.1) was trained with 1.5 million games of self-play, and achieved a level of play just slightly below that of the top human backgammon players of the time. In 1998, during a 100-game series, it was defeated by the world champion by a mere margin of 8 points. Its unconventional assessment of some opening strategies had been accepted and adopted by expert players. TD-gammon is commonly cited as an early success of reinforcement learning and neural networks, and was cited in, for example, papers for deep Q-learning and AlphaGo. Algorithm for p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure and functions of biological neural networks. A neural network consists of connected units or nodes called '' artificial neurons'', which loosely model the neurons in the brain. Artificial neuron models that mimic biological neurons more closely have also been recently investigated and shown to significantly improve performance. These are connected by ''edges'', which model the synapses in the brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The "signal" is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs, called the '' activation function''. The strength of the signal at each connection is determined by a ''weight'', which adjusts during the learning process. Typically, ne ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evaluation Function
An evaluation function, also known as a heuristic evaluation function or static evaluation function, is a function used by game-playing computer programs to estimate the value or goodness of a position (usually at a leaf or terminal node) in a game tree. Most of the time, the value is either a real number or a quantized integer, often in ''n''ths of the value of a playing piece such as a stone in go or a pawn in chess, where ''n'' may be tenths, hundredths or other convenient fraction, but sometimes, the value is an array of three values in the unit interval, representing the win, draw, and loss percentages of the position. There do not exist analytical or theoretical models for evaluation functions for unsolved games, nor are such functions entirely ad-hoc. The composition of evaluation functions is determined empirically by inserting a candidate function into an automaton and evaluating its subsequent performance. A significant body of evidence now exists for several games ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Chess
Computer chess includes both hardware (dedicated computers) and software capable of playing chess. Computer chess provides opportunities for players to practice even in the absence of human opponents, and also provides opportunities for analysis, entertainment and training. Computer chess applications that play at the level of a Chess title, chess grandmaster or higher are available on hardware from supercomputers to Smartphone, smart phones. Standalone chess-playing machines are also available. Stockfish (chess), Stockfish, Leela Chess Zero, GNU Chess, Fruit (software), Fruit, and other free open source applications are available for various platforms. Computer chess applications, whether implemented in hardware or software, use different strategies than humans to choose their moves: they use Heuristic (computer science), heuristic methods to build, search and evaluate Tree (data structure), trees representing sequences of moves from the current position and attempt to execute ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
AlphaZero
AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and Go (game), go. This algorithm uses an approach similar to AlphaGo Zero. On December 5, 2017, the DeepMind team released a preprint paper introducing AlphaZero, which would soon play three games by defeating world-champion chess engines Stockfish (chess), Stockfish, Elmo (shogi engine), Elmo, and the three-day version of AlphaGo Zero. In each case it made use of custom tensor processing units (TPUs) that the Google programs were optimized to use. AlphaZero was trained solely via Self-play (reinforcement learning technique), self-play using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel computing, parallel, with no access to Chess opening book (computers), opening books or Endgame tablebase, endgame tables. After four hours of training, DeepMind estimated AlphaZero wa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Olympiad
The Computer Olympiad is a multi-games event in which computer programs compete against each other. For many games, the Computer Olympiads are an opportunity to claim the "world's best computer player" title. First contested in 1989, the majority of the games are board games but other games such as bridge take place as well. In 2010, several puzzles were included in the competition. History Developed in the 1980s by David Levy, the first Computer Olympiad took place in 1989 at the Park Lane Hotel in London. The games ran on a yearly basis until after the 1992 games, when the Olympiad's ruling committee was unable to find a new organiser. This resulted in the games being suspended until 2000 when the Mind Sports Olympiad resurrected them. Recently, the International Computer Games Association (ICGA) has adopted the Computer Olympiad and tries to organise the event on an annual basis. In the year 2024, parody websitMike Row Softadded an image of the Olympics with various Li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Supervised Learning
In machine learning, supervised learning (SL) is a paradigm where a Statistical model, model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a ''supervisory signal''), which are often human-made labels. The training process builds a function that maps new data to expected output values. An optimal scenario will allow for the algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to Generalization (learning), generalize from the training data to unseen situations in a reasonable way (see inductive bias). This statistical quality of an algorithm is measured via a ''generalization error''. Steps to follow To solve a given problem of supervised learning, the following steps must be performed: # Determine the type of training samples. Before doing anything else, the user should decide what kind of data is to be used as a Training, validation, and test data sets, trainin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neurogammon
Neurogammon is a computer backgammon program written by Gerald Tesauro at IBM's Thomas J. Watson Research Center. It was the first viable computer backgammon program implemented as a neural net, and set a new standard in computer backgammon play. It won the 1st Computer Olympiad in London in 1989, handily defeating all opponents. Its level of play was that of an intermediate-level human player. Neurogammon contains seven separate neural networks, each with a single hidden layer. One network makes doubling-cube decisions; the other six choose moves at different stages of the game. The networks were trained by backpropagation from transcripts of 400 games in which the author played himself. The author's move was taught as the best move in each position. In 1992, Tesauro completed TD-Gammon, which combined a form of reinforcement learning with the human-designed input features of Neurogammon, and played at the level of a world-class human tournament player. See also *World Backgamm ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Thomas J
Clarence Thomas (born June 23, 1948) is an American lawyer and jurist who has served since 1991 as an associate justice of the Supreme Court of the United States. President George H. W. Bush Clarence Thomas Supreme Court nomination, nominated him to succeed Thurgood Marshall. After Marshall, Thomas is the second African Americans, African American to serve on the U.S. Supreme Court and has been its List of United States Supreme Court justices by time in office, longest-serving member since Anthony Kennedy's retirement in 2018. He has also been the Court's oldest member since Stephen Breyer retired in 2022. Thomas was born in Pin Point, Georgia. After his father abandoned the family, he was raised by his grandfather in a poor Gullah community near Savannah, Georgia. Growing up as a devout Catholic, Thomas originally intended to be a priest in the Catholic Church but became dissatisfied with its efforts to combat racism and abandoned his aspiration to join the clergy. He gradua ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Terrence Sejnowski
Terrence Joseph Sejnowski (; born 13 August 1947) is the Francis Crick Professor at the Salk Institute for Biological Studies where he directs the Computational Neurobiology Laboratory and is the director of the Crick-Jacobs center for theoretical and computational biology. He has performed research in neural networks and computational neuroscience. Sejnowski is also Professor of Biological Sciences and adjunct professor in the departments of neurosciences, psychology, cognitive science, computer science and engineering at the University of California, San Diego, where he is co-director of the Institute for Neural Computation. In 2025, he was elected to the American Philosophical Society. With Barbara Oakley, he co-created and taught ''Learning How To Learn: Powerful mental tools to help you master tough subjects'', the world's most popular online course, available on Coursera. Early life and education Sejnowski was born in Cleveland in 1947. Sejnowski received a Bachelor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
NeurIPS
The Conference and Workshop on Neural Information Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held every December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. The conference is currently a double-track meeting (single-track until 2015) that includes invited talks as well as oral and poster presentations of refereed papers, followed by parallel-track workshops that up to 2013 were held at ski resorts. History The NeurIPS meeting was first proposed in 1986 at the annual invitation-only Snowbird Meeting on Neural Networks for Computing organized by The California Institute of Technology and Bell Laboratories. NeurIPS was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
