Arthur Samuel's checkers program: Learning from a game of checkers

The event and the narrow documented claim

In July 1959, IBM researcher Arthur L. Samuel published Some Studies in Machine Learning Using the Game of Checkers in the IBM Journal of Research and Development. The documented claim of that paper is specific and modest: a program written for the IBM 701 could adjust its own behavior from prior play and, in doing so, come to play checkers better than the version that started, and in some accounts better than the person who wrote it. The contribution was not that a machine could play a board game, because others had already tried that, but that a machine could improve at one without a person rewriting its decision rules by hand.

That distinction is the whole point, and it is worth stating in sober terms. Samuel demonstrated learning inside a single, fully specified game with fixed rules and unambiguous win, loss, and draw outcomes. He did not claim general intelligence, and the popular framing that he "coined machine learning" is best read as a matter of title and emphasis rather than a formal definition. The line often quoted from him, about giving computers "the ability to learn without being explicitly programmed," does not appear verbatim in the 1959 text; the durable fact is that the paper put the words machine learning in front of the research community with a working demonstration attached. IBM's own history describes the program as where Samuel coined the term. The useful historical question is therefore not whether one paper began machine learning, but which constraint it addressed, which tools it inherited, and which later results genuinely depended on it.

Predecessors: Shannon's evaluation function and Strachey's draughts

Samuel was not working in a vacuum. The conceptual scaffolding for computer game play had been set out a decade earlier by Claude Shannon in Programming a Computer for Playing Chess, published in the Philosophical Magazine in March 1950. Shannon's paper was theoretical, describing no running program, but it defined the two ideas Samuel would build on: a numerical evaluation function that scores a position without playing it to the end, and a search that looks ahead a bounded number of moves and backs up scores under the assumption that each side chooses its best available move. Shannon distinguished a brute "type A" search that examines everything to a fixed depth from a selective "type B" search that prunes unpromising lines. Samuel's work is in large part an empirical study of how to make those abstractions run on real, memory-starved hardware.

On the playing side, checkers already had a working precedent. Around 1951 and 1952 Christopher Strachey wrote a draughts program for the Ferranti Mark 1 at Manchester, which by the summer of 1952 could play a complete game at a usable speed. Strachey's program searched and evaluated but did not learn: it played the same way every game. Reading Samuel against these two predecessors sharpens the claim. Shannon supplied the search-and-evaluate framework, Strachey showed a machine could play the specific game, and Samuel's separable addition was a mechanism by which the evaluation itself changed with experience.

The constraint: search alone could not fit the machine

The reason learning mattered here is a hardware constraint, not a philosophical one. Checkers has on the order of 5 times 10 to the 20th legal positions, which is far too many to enumerate, so a program cannot simply look ahead to the end of every game and pick a winning line. It must stop the search early and estimate the value of a non-terminal position, and the quality of that estimate is what separates a strong program from a weak one. On the IBM 701, a vacuum-tube machine with tiny memory by any later standard, even a shallow full-width search was expensive, which is exactly why a good static evaluation and aggressive pruning were not luxuries but necessities.

Samuel's insight was that the evaluation function is the hard part and the part least suited to hand-tuning. A programmer can encode that a piece is worth something and that kings are worth more, but the relative weight of mobility, center control, exchange opportunities, and back-row integrity is not obvious and shifts across the phases of a game. Rather than have a human guess those weights, Samuel let the program discover them from the record of its own games. The constraint that search cannot reach the end of checkers is what makes a learned evaluation valuable, and it is the same constraint that still shapes game-playing systems today.

How the program actually learned: rote and generalization

The 1959 paper describes two distinct learning methods, and keeping them separate is important because they became separate lineages in later research. The first, which Samuel called rote learning, simply stored positions the program had already analyzed together with the backed-up score it had computed for each. When one of those positions reappeared, the program could retrieve the stored value instead of recomputing it, which in effect let a shallow search reach deeper by standing on the results of earlier searches. Rote learning was memory-bound: Samuel reported storing on the order of tens of thousands of positions, and its gains were real but limited by how much could be remembered and how often stored positions recurred.

The second and more consequential method was learning by generalization. Here the program did not memorize positions but adjusted the numerical weights of the terms in its evaluation function so that its estimate of a position agreed better with the value implied by looking further ahead from that same position. In modern language, the program nudged an earlier prediction toward a later, presumably more reliable prediction of the same game's outcome. That comparison of temporally successive estimates, rather than a comparison against a final labeled answer, is the idea later formalized as temporal-difference learning, and Samuel's checkers player is routinely cited as its first working instance in Reinforcement Learning: An Introduction by Sutton and Barto.

The evaluation polynomial and an early form of alpha-beta

The mechanics are worth stating concretely, because the paper's credibility rests on them. The evaluation was a linear polynomial: a weighted sum of board features, with piece advantage as the dominant term and a rotating set of positional features filling the rest. Samuel maintained a larger pool of candidate features than the polynomial used at any one time, and the learning procedure not only tuned the weights but also swapped out terms that repeatedly earned near-zero weight in favor of currently unused ones, an early form of automatic feature selection. Training happened largely through self-play, one copy of the program using the current weights against another that held its weights fixed, with the winner's weight vector carried forward.

To make the search affordable, Samuel used minimax lookahead with pruning that anticipates the modern alpha-beta algorithm: once a line is shown to be worse than one already available, the rest of that line need not be examined. His later paper, Some Studies in Machine Learning Using the Game of Checkers, II, Recent Progress, published in 1967, states that the revised program made full use of alpha-beta pruning and added a signature-table representation that replaced the linear polynomial with a scheme able to capture interactions between features. The two papers together are the primary record; the 1967 paper is where several of the techniques reached their more familiar form and should not be collapsed into the 1959 date.

The evidence, and the numbers to treat carefully

What did the program actually achieve, and how strong is the evidence? The honest answer is that Samuel reported qualitative and comparative progress more than a single headline benchmark. He showed that the generalizing program improved with play, that it could beat the rote-learning version and unlearned baselines, and that it reached a level he characterized as a better-than-average amateur game. These are task-specific results about one game under stated training conditions, and they should be read that way rather than as a measure of intelligence in any general sense. The paper's strength is methodological: it isolates the learning mechanism, compares configurations against each other, and reports where the approach helped and where it did not.

Two cautions belong with the numbers. First, self-play evaluation can flatter a system, because a program that has learned to exploit its own habits is not the same as one that generalizes to unfamiliar opponents. Samuel was aware of this and tested against outside play, but the strongest claims about human-competitive strength come from a single later exhibition game rather than from systematic tournament results. Second, the learning was narrow by construction. It optimized an evaluation for checkers; it did not transfer to other tasks, and Samuel did not suggest it would. The value of the evidence is that it made an abstract idea, a program that improves from experience, concrete and reproducible, not that it settled how strong such a program could ultimately become.

The 1962 Nealey game and why it is misremembered

The most repeated story about Samuel's program is also the most distorted, and the grounding for this event flags it directly: the 1962 win over a human opponent should not be assigned to the 1959 publication. In 1962 the program defeated Robert Nealey, and IBM publicity described Nealey as a checkers master, which helped the result harden into legend. The Chinook project's account of Samuel's legacy documents the correction: Nealey was not a state champion at the time (he won the Connecticut title in 1966, four years later), did not compete in the premier events, and the program itself made errors in the very game it won. When the program later faced genuine world-championship-caliber players in 1966, it lost every game.

This matters for two reasons. It is a case study in how a single exhibition result, amplified by a corporate press office, can create a false impression, in this instance the impression that checkers was effectively a solved or conquered game, which persisted for decades and even shaped later funding judgments. And it is a reminder to keep the dates straight: the enduring scientific contribution is the 1959 learning methodology, not a 1962 game whose significance was overstated at the time. A history that leads with the Nealey game gets the emphasis backward.

Competing approaches of the era

Samuel's learning-from-play approach was one strand in a young field that was actively debating how machines might be made intelligent, and it looked unusual against the dominant alternative. The same period saw the rise of symbolic artificial intelligence, whose founding document, the Dartmouth summer research proposal circulated in 1955 by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, framed intelligence largely in terms of reasoning, logic, and the manipulation of symbols. Programs such as the Logic Theorist pursued intelligence by searching for proofs and applying explicit rules, an approach in which the knowledge is written down by the designer.

Samuel's checkers work sat somewhat apart from that program. It was statistical and adaptive rather than logical and hand-specified: the knowledge was encoded as adjustable numbers that the machine tuned from data, not as symbolic rules a human authored. For much of the following three decades the symbolic tradition, and later the expert-systems movement it inspired, drew the larger share of attention and funding, while numerical, learning-based methods remained a minority interest. Placing Samuel correctly means recognizing that his approach was closer in spirit to the machine learning that would later dominate than to the symbolic reasoning that dominated his own era, which is part of why the 1959 paper reads as ahead of its moment rather than typical of it.

Downstream influence: from temporal differences to a solved game

The clearest evidence that the event mattered lies in specific later work that reused its ideas while changing an ingredient. The generalization method's habit of adjusting an estimate toward a later estimate of the same outcome was formalized by Richard Sutton in Learning to Predict by the Methods of Temporal Differences in 1988, a paper that explicitly names Samuel's checkers player as a precursor to the class of algorithms it analyzes. Temporal-difference learning then produced a striking demonstration of its own: Gerald Tesauro's TD-Gammon, described in 1995, trained a neural network by temporal-difference learning through self-play and reached a standard of backgammon comparable to the best human players, a direct methodological descendant of what Samuel had prototyped by hand.

The other line of descent runs through the game itself. In 2007 Jonathan Schaeffer and colleagues reported in Checkers Is Solved that perfect play by both sides leads to a draw, a result established through a computation touching the game's full space of roughly 5 times 10 to the 20th positions. That work closed, with proof, the question the 1962 publicity had prematurely declared answered. Read together, these papers show two forms of influence: a conceptual one, in which Samuel's learning rule became a named and widely used algorithm, and a domain one, in which checkers remained a proving ground for game-playing research until it was exhaustively solved.

Honest limits and how to place the event

The caveats should be stated as plainly as the achievement. Samuel's program learned one game, with a human-designed feature vocabulary and a linear evaluation that the machine tuned but did not invent; the features themselves were chosen by a person, so the system learned weights rather than representations. Its most cited human result was overstated. Its self-play evaluation had the usual blind spots. And the phrase most associated with it, machine learning as a self-describing definition, is a later gloss on a paper whose real content is a careful empirical study. None of this diminishes the work; it locates it. The primary IBM record and a contemporary account of the 1956 televised demonstration on the IBM 701, which drew public attention to the program well before the 1959 paper, both help fix the timeline without inflating any single moment.

So how should a reader place samuel-checkers in the history of artificial intelligence? As a genuine origin point for the practice of getting a program to improve from experience, demonstrated concretely enough to be reproduced and extended, and as an early public face of the idea. Not as the invention of intelligence, not as proof that checkers was conquered, and not as a single leap that made what followed inevitable. The most reliable way to navigate outward from it is to ask which later result reused the learning rule (Sutton's temporal-difference analysis and Tesauro's TD-Gammon), which later result answered a question this event only raised (Schaeffer's proof that checkers is a draw), and which parts of the story were myth from the start (the Nealey game). Kept at that altitude, the 1959 paper is exactly what it claimed to be: a set of careful studies in machine learning, using the game of checkers.

References

• Samuel, A. L. (1959). Some Studies in Machine Learning Using the Game of Checkers.
• Samuel, A. L. (1967). Some Studies in Machine Learning Using the Game of Checkers, II, Recent Progress.
• IBM (2023). Early games.
• Shannon, C. E. (1950). Programming a Computer for Playing Chess.
• Chess Programming Wiki (2024). Christopher Strachey.
• McCarthy, J., Minsky, M., Rochester, N. and Shannon, C. (2006). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955.
• Sutton, R. S. (1988). Learning to Predict by the Methods of Temporal Differences.
• Tesauro, G. (1995). Temporal Difference Learning and TD-Gammon.
• Sutton, R. S. and Barto, A. G. (2018). Reinforcement Learning: An Introduction.
• Schaeffer, J. et al. (2007). Checkers Is Solved.
• Chinook Project, University of Alberta (2007). Arthur Samuel's Legacy.
• History of Information (2023). Arthur Samuel Proves that Machines Can Learn from Past Errors.