Some Studies in Machine Learning Using the Game of Checkers

By Arthur L. Samuel. 1995

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In Reinforcement Learning

Citations in the corpus

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, background): “A long-standing ambition of artificial intelligence has been to create programs that can instead learn for themselves from first principles (28).”
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “The strength of ML resides in its capacity to learn from data without need to be explicitly programmed (Samuel, 1959); ML algorithms are autonomous and self-sufficient when performing their learning function.”
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, background): “Games have a tradition of encouraging advances in artiﬁcial intelligence and machine learning (Samuel, 1959; Tesauro, 1995; Campbell et al., 2002; Gelly et al., 2012; Bowling et al., 2015).”
Reinforcement Learning: A Survey (1996, background): “One application, spectacularly far ahead of its time, was Samuel's checkers playing system (Samuel, 1959).”
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play (2017, background): “The following experiments explore our self-play approach on a variety of tasks, both continuous and discrete, from the Mazebase [21], RLLab [5], and StarCraft [23] environments.”
Infinite-Horizon Policy-Gradient Estimation (2001, background): “ture (Bertsekas & Tsitsiklis, 1996; Sutton & Barto, 1998). This approach has yielded some remarkable empirical successes in a number of different domains, including learning to play checkers (Samuel, 1959), backgammon (Tesauro, 1992, 1994), and chess (Baxter, Tridgell, & Weaver, 2000), j”
Behaviour Suite for Reinforcement Learning (2019, background): “The advances in RL systems have been similarly impressive: from checkers (Samuel, 1959), to Backgammon (Tesauro, 1995), to Atari games (Mnih et al., 2015a), to competing with professional players at DOTA (Pachocki et al., 2019) or StarCraft (Vinyals et al., 2019) and beating world champions at Go…”
Optimizing agent behavior over long time scales by transporting value (2018, background): “(4) In general, the half-life (strictly, the 1/e-life) of reward in units of time steps is τ = 1 1−γ .”
Byzantine-Tolerant Machine Learning (2017, background): “Roughly speaking, machine learning consists in giving a computer the ability to improve the way it solves a problem with the quantity and quality of information it can use [17].”
Learning to Search with MCTSnets (2018, methodology): “Many success stories in artiﬁcial intelligence are based on the application of powerful tree search algorithms to challenging planning problems (Samuel, 1959; Knuth & Moore, 1975; J¨unger et al., 2009).”
Emergent Coordination Through Competition (2019, background): “Competitive games have been grand challenges for artificial intelligence research since at least the 1950s (Samuel, 1959; Tesauro, 1995; Campbell et al., 2002; Vinyals et al., 2017).”
Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data (2013, background): “Related work that reaches back fifty years [15–18] investigates supervised feature learning, where the expert selects the learning task, a set of raw input variables and the outcome labels, and then the algorithm tries to construct features from the input variables either individually [12,15,16] or…”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, neutral): “Originally the term ‘‘machine learning’’ was deﬁned as ‘‘... artiﬁcial generation of knowledge from experience,’’ and the ﬁrst studies have been performed with games, i.e., with the game of checkers [1].”
WRITING MEMORIES WITH LIGHT-ADDRESSABLE REINFORCEMENT CIRCUITRY (2009, background): “Engineers (Kalman, 1960), researchers in machine learning (Samuel, 1959; Holland, 1986; Sutton and Barto, 1998), and psychologists (Rescorla and Wagner, 1972) have developed models of adaptive behavior in which error signals play a central role; they have also designed efficient algorithms for their”
Temporal Difference Learning of Position Evaluation in the Game of Go (1993, background): “(Samuel, 1959) found it inadvisable to let his checker program learn from games which it won against an opponent, since its predictions might otherwise reflect poor as well as good play.”