06 June 2025
The average chess players of Bletchley Park and AI research in Britain
There is a good chance that AI research in Britain would not have evolved the way it did had Alan Turing been a great chess player. As a matter of fact, he couldn’t hold a candle to the chess masters, such as Hugh Alexander and Harry Golombek, who were in his Enigma codebreaking team at Bletchley Park (BP). As per one account, Turing once played himself into such a mess that his opponent Golombek turned the board around and tried to save the game for him.
However, Turing’s mediocrity in chess proved to be a blessing for one of the youngest codebreakers at BP, Donald Michie, who was just 18-years-old when he was recruited to the Government Code and Cypher School. Despite having no mathematical background, he proved to be a quick learner and eventually became a key member of Testery — Major Ralph Tester’s team attempting to break the ‘Fish’ codes using manual hand methods. It was during his time at Testery that he met and befriended Turing, who had developed Turingery — one of the manual methods used by codebreakers of Testery.
While Turing was no match for his chessmaster teammates, in young Michie, he found a partner who could give him a competitive game. They met every week at a pub in Wolverton not too far away from BP. This friendship and weekly chess session proved to be life-changing for Michie.
According to Michie, their conversations invariably centred around ideas of learning machines and mechanising chess, ideas formative to his interest and eventual career in machine intelligence.
Turing and Michie remained friends after the war.
Post-war games and game-playing machines
In the period of 1947-48, Michie and the mathematician Shaun Wylie, another colleague at BP, developed a chess-playing algorithm, or a machine on paper — a ‘paper machine’, so to speak. This was basically a collection of machine-rules to decide the next move using the opponent’s move as input. The ‘MACHIAVELLI’ (so named after creators Michie and Wylie), was created independently of the paper-machine that Turing and his friend David Champernowne developed, named ‘TUROCHAMP.’
I. J. ‘Jack’ Good, who was friends with both Michie and Turing, too took part in the BP discussions of ‘chess-playing’ machines. In fact, Jack Copeland notes that Good recalls Turing speaking about the concept of mechanizing chess in 1941, well before Michie’s time at BP. Like Michie, Good was interested in both chess and ‘learning’ machines. He was an excellent chess player apart from being a brilliant mathematician. Good was aware of the challenge that Michie and Wylie made to Turing and Champerowne’s chess-machine, and suggested a way in which MACHIAVELLI could be beaten in a letter he wrote to Turing in 1948:
I visited Oxford last week-end. Donald showed me a 'chess machine' invented by Shaun and himself. It suffers from the very serious disadvantage that it does not analyse more than one move ahead. I am convinced that such a machine could play a very poor game, however accurately it scored the position with respect to matter and space. In fact it could easily be beaten by playing 'psychologically', i.e. by taking into account the main weakness of the machine. This could be done by deliberately complicating the position and entering into combinations.
The correspondences show how the discussions on mechanizing chess from the BP years, evolved during the post-war period. However, these were not just the result of Michie and Turing’s acquaintance with chess players during their time at BP.
They were also shaped very much by the kind of codebreaking work they were involved in.
Both Michie’s and Turing’s chess-playing machines involved taking the opponent’s move as input and then creating an output move in response. Both of the algorithms involved ‘searching’ for a good move after considering various possible single moves by ranking them based on variables such as the safety of the piece, and the value of opponent’s pieces that could be captured. This was a guided search for ‘moves’ or ‘solutions’ using an evaluating – or ‘heuristic’ – function to narrow the number of possibilities rather than completing an exhaustive general search through all possible moves. It was conceptually very similar to the machine-aided searches that codebreakers at BP had to conduct to break the German ciphers. Michie’s job, being a key member of Newmanry team that broke the Lorenz cipher using the digital codebreaking machine Colossus, was to come up with solutions to narrow down the daily codebreaking searches of the machines. Such a problem-solving technique that uses a heuristic function to estimate hopeful paths to a solution would eventually be termed as "heuristic search". Michie himself would go on to make a significant contribution to developing and popularising this computational technique during the 1960s with his graph traverser program co-developed with J. E. Doran.
Going through Michie’s archives, we can find that research into chess-machines remained relevant to his entire machine intelligence and AI research career, even during the period that he worked as a geneticist. For instance, during the 1950s he had played MACHIAVELLI against biologist John Maynard Smith’s SOMA (Smith’s One-Move Analyser) in matchup refereed by Maynard Smith’s eldest son. A detailed description of how the machines performed was given in the 1961 New Scientist that Michie wrote together with Maynard Smith: ‘Machines that play games’.
Importance of chess to AI and machine learning
Chess became central to Michie’s research in the 1970s, when his research program was curtailed by the University of Edinburgh in the aftermath of the Lighthill Report that drastically defunded AI research in the UK. Chess endgames research was something Michie could work on with the limited funding he had post-Lighthill. However, for Michie, chess was not just a convenient and playful way to engage his interest in machine intelligence, as he clearly puts it in a 1980 draft titled ‘A representation for pattern knowledge in chess end-games’. In it, he addresses the question of whether those involved in computer chess is just ‘fooling around with the taxpayer’s money,’ and argues that ‘no other equally apposite material is readily available for investigating certain scientific issues of importance.’
This is also a point he emphasised in his 1966 paper, ‘Game-playing and game-learning automata’, referring to Turing’s interest in machines that could play games,:
It is sometimes thought that Turing’s interest in mechanised game playing was the spare time frivolity of a man who reserved his serious thoughts for worthier topics. That was not the case. He had the conviction that the development of high-speed digital computing equipment would make possible the mechanisation of human thought processes of every kind, and that games constituted an ideal model system in which studies of machine intelligence could first be developed.
Chess, for Michie, was the “fruit-fly” of AI research that was perfect for “studying the representation and measurement of knowledge in machines.’’ In another 1980 article ‘Chess with Computers’, he describes in detail why the strategic game is ideal for AI research and its “chief advantages”:
…chess constitutes a well-defined and formalized domain; it challenges the highest levels of intel1ectual capacity over a wide range of cognitive functions logical concept-formation, calculation, rote-learning, analogical thinking, deductive and inductive reasoning, and so forth; a detailed corpus of chess knowledge has accumulated over centuries in chess instructional works and commentaries; a generally accepted numerical scale for performance is available in the USCF rating system; and finally, the game can readily be decomposed into sub-games which can be subjected to intensive separate analysis.
Michie’s focus on chess-endgames research would eventually contribute to the development of the groundbreaking Iterative Dichotomiser 3 (ID3) learning algorithm for generating decision trees by J. Ross Quinlan, who was one of the many brilliant researchers Michie mentored during his career. In Quinlan’s 1986 paper, he acknowledges Michie’s role in the endeavour:
ID3 (Quinlan, 1979, 1983a) is one of a series of programs developed from CLS in response to a challenging induction task posed by Donald Michie, viz. to decide from pattern-based features alone whether a particular chess position in the King-Rook vs King-Knight endgame is lost for the Knight's side in a fixed number of ply.
This shows how chess shaped the fields of AI and machine learning; chess endgames research also played a key role in the projects of many of Michie’s PhD students from the 1970s and 80s, including Stephen Muggleton, Alan Shapiro and Tim Niblett’s pioneering work on induction methods in machine learning.
Nevertheless, the relationship between chess and AI research remains severely underexplored in the history of AI.
Posted by Aswin Valsala Narayanan
Further reading:
Donald Michie, “Alan Turing’s Mind Machines,” February 8, 2008, https://doi.org/10.7551/mitpress/7626.003.0005.
Herbert A. Simon and Allen Newell, “Heuristic Problem Solving: The Next Advance in Operations Research,” Operations Research 6, no. 1 (1958): 1–10.
Donald Michie, “Game-Playing and Game-Learning Automata,” in Advances in Programming and Non-Numerical Computation (Elsevier, 1966), 183–200.
Donald Michie, “Chess with Computers,” Interdisciplinary Science Reviews 5, no. 3 (January 1, 1980): 215–27, https://doi.org/10.1179/isr.1980.5.3.215.
J. R. Quinlan, “Induction of Decision Trees,” Machine Learning 1, no. 1 (March 1, 1986): 81–106, https://doi.org/10.1007/BF00116251.
The Donald Michie Papers at the British Library comprise two separate tranches of material gifted to the Library in 2004 and 2008. They contain correspondence, notes, notebooks, offprints and photographs and are available to researchers.
Aswin Valsala Narayanan is as PhD student at the University of Leeds and the British Library. He is on an WRoCAH Collaborative Doctoral Award researching the Donald Michie Archive, exploring Michie's work as an artificial intelligence researcher in post-war Britain.