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Machines That Play series has been broken into 7 parts.
This series covers the history of Artificial Intelligence and games (until Deep Blue) and focuses on machines that played chess, checkers, and backgammon. The following topics are covered: how to build chess machines, Shannon’s work on chess, Turing’s work on chess, The Turk, El Ajedrecista, MANIAC, Bernstein chess program, Samuel’s checkers, Mac Hack VI, Cray Blitz, BKG, HiTech, Chinook, Deep Thought, TD-Gammon, and Deep Blue.
This is Part 1 of the series and gives an overview of AI efforts related to games: chess, checkers, backgammon. It also asks (and attempts to answer) the following questions: What’s an ideal game? Why are we interested in artificial intelligence (AI) and games?
Before we talk about games and machines, let’s first talk about games and humans.
What’s an ideal game?
A game is something with rules and an objective. We “play” a game when we perform actions, constrained by these rules, in order to achieve the established objective.
We (humans) seem to need play almost as much as we need food, water, air, shelter, and tools to survive. What then, for us humans, would make a game ideal? This is a hard question to answer, but I imagine that an ideal game may have at least some of the following characteristics:
A game in which all players are highly skilled (or at similar levels): A game in which a player is able to use his skill to overcome the challenge that his opponent(s) offer. Ideal pleasure lies using his skill to outsmart or outmaneuver an opponent who is also very good at the game.
A game that is neither too easy, nor too hard so a player would “just win”: A game in which a player beats his opponent(s), who also performs close to his skill level. It’s a game he could have lost, but doesn’t lose. He wins. And he wins because the game allows him to rise just slightly more than his opponent(s); he conquers the elements of chance and exercises control over his environment (and himself).
A game that forces a player to develop and reach his highest potential in order to win: A game in which a player develops highest skills to outsmart or outmaneuver his opponent(s). Ideal growth means he is able to realize his potential, to be all that he can be. He develops such great skill that the risk of failure is nearly eliminated.
A game that changes a player’s psyche: A game that alters a player’s state of mind and in addition to realizing his highest potential, the game allows him to immerse himself fully in order to succeed. It is a game he would choose to continue playing as long as he can so he can accomplish (what he would later call) meaningful actions. And after the game, he would notice that he has changed from the game-playing experience and that his mind has been enriched with new skills, new experiences and new achievements.
A game that forces a player to be a catalyst for global change (remember this is an ideal game): A game in which the player would need to transform (or even transcend) himself, others, or even the entire world to the fullest extent in order to win. An ideal game would involve the player realizing his (and humans’) deepest yearnings, passions, or values. If not this, then the game would at least consume him so deeply as to free him from the chains of his everyday life. And through his journey he would create a path for others to develop and realize their potential.
Is there an ideal game?
I don’t know if there is an ideal game, but, in my opinion, the following example comes pretty close to being one; it not only satisfies many of the characteristics listed earlier, but it challenges and blurs those same characteristics.
Death: Well, I am a pretty skilled chess player.
Knight: But I bet you’re not as good as me.
Death: Why do you want me to play chess with me?
Knight: That’s my business.
Knight: The condition is that you let me live for as long as I can stand against you.
Knight: If I win, you let me go.
The player chooses to play as long as he lives (or can). The stakes are too high and he cannot help but still try.
We are never playing against Death (and almost winning). Our actions, in most games, are not nearly as “meaningful” as saving the lives of other humans. Our games neither give us an opportunity to come to terms with our inescapable despair nor to radically transform (or transcend) ourselves or lives of others.
In reality there is probably no ideal game. Why do we still play then? A vague and simplistic answer is “because games are fun and/or useful”, but that doesn’t seem enough. We continue to both create and play games and we continue because games still demonstrate different combinations of characteristics stated above.
Some of our oldest games
We’ve always played games. Games are one of our oldest sources of play. It’s possible that earlier humans played games to practice skills that prepared them for hunting and combat. For example, the bow and arrow was invented by the end of the Upper Paleolithic.
Archery is the skill of using bows to shoot arrows. In most prehistoric cultures, archery was an important military and hunting skill. Practicing archery (or playing to practice or improve) would have improved the archer’s probability of success. In this sense, such physical games may have been played to increase our chance of survival and success.
At a later time, humans began to settle in one place, which meant they weren’t moving around as much. This gave them some routine and physical games began to translate to board games. These games tapped into a variety of our desires. Some of the oldest games are:
Senet (~3100 BC): The full name of this race board game means the “game of passing”. Ancient Egyptians believed in an afterlife and the journey to the afterlife required the person who had died to perform rituals and pass obstacles. By the time of the New Kingdom in Egypt, senet was viewed as a representation of the journey to the afterlife. It seems senet was not just a game, it represented our struggle to achieve immortality of some kind. [Games of this type tap into our innate desire to survive beyond this human state.]
Royal Game of Ur (~3000 BC): This is a two-player strategy race board game. The Game of Ur received its name because it was discovered by the English archaeologist Sir Leonard Woolley during his excavations of the Royal Cemetery at Ur between 1922 and 1934. At some point people began to attach spiritual significance to the game and events in the game were believed to reflect the player’s future as well as convey messages from supernatural beings. [Games of this type tap into our yearning to know and control our future.]
Mancala (~6th century AD): Mancala is not any one game, it is the classification or type of a game: any 2-player turn-based strategy board game. The goal is to capture all or some set of the opponent’s pieces (seeds, stones, beans, etc.). [Games of this type tap into our ancient desire to survive by building shelters, hunting, gathering food, and winning.]
We played (and continue to play) games for a wide variety of reasons:
We play to experience delight (fun)
We play to eliminate boredom / to escape reality
We play to practice and improve skills
We play to learn how to think critically and strategically
We play to conquer uncertainty, chance, luck, and probability
We play to play to create things in order to empower ourselves.
We play to destroy things in order to lessen our anger and frustration
We play to settle differences
We play to collaborate
We play to sate all of our human desires (prehistoric and current): nurture, hunt, kill, conquer, combat, compete, collaborate, create, survive
We play to win — to feel a sense of achievement
Throughout history we have created and played games to challenge our intelligence, strength, strategy, emotions, and more. In games, we come together and agree to a set of arbitrary rules. We compete and collaborate, we strategize to conquer chance and uncertainty, we set and achieve goals, we exercise imagination and experience delight of success.
Why AI and games?
Games are hard. Games are interesting. Games are test-beds for AI.
As technology evolved, so did our games. Recent technology has provided us with new teammates as well as new opponents, in form of machines. Even though the history of games is fascinating, we’ll focus on automation, artificial intelligence (AI), and games in this series. More specifically, we’ll focus on games where AI has either learned to play just as well as us, or better. This journey will turn out to serve as a humble reminder:
No matter what the rate of improvement is for humans, once machines begin to learn, it will become hard for us to keep up with them — their learning and progress will end up being measured exponentially. And ours won’t.
Since the earliest days of computing, people wondered whether machines could match or surpass human intelligence. Artificial intelligence is about building machines that are able to perform the tasks that (we think) require “intelligence”. But earlier AI approaches and algorithms were not sufficient to tackle real-world problems because of their complex and ambiguous nature. Programming machines to play games successfully served as one way for computers to learn tactics and strategies that could later be applied to other real-life domains.
Emulate human thought process in games
Early AI researchers emphasized emulation of human thought process in games because they believed the best game-playing machines can be created by teaching them how to mimic human thought. They reasoned that if machines could tackle games successfully then they would most likely exhibit some type of intelligence.
Understand how the human mind works
Early AI researchers hoped that programming machines to play games successfully would help understand how the human mind worked, how it thought, how it solved problems, and ultimately what intelligence was. They assumed that building machines to perform tasks that required intelligence would provide a glimpse into how our own intelligence worked.
We will see that even when machines surpassed humans in games, they did not necessarily give insight into the workings of our minds. They did, however, help push progress in computer science (and hence other related fields). And later, the research helped us tackle some complex real-world problems head-on.
“Games are fun and they’re easy to measure. It’s clear who won and who lost, and you always have the human benchmark…Can you do better than a human?” Murray Campbell
Games, board games specifically, are one of the oldest branches of AI, starting with Shannon and Turing 1950. They provided a good way to measure the capacity of AI ideas because of 1) their simplicity of objective, 2) well-defined rules and 3) the huge range of possible strategies to reach the final objective. Every time AI conquered a game, it helped us tackle at least some complex real-world problems head-on.
The state-space complexity of a game is the number of legal game positions reachable from the initial position of the game.
The game tree size is the total number of possible games that can be played: the number of leaf nodes in the game tree rooted at the game’s initial position.
The branching factor is the number of children at each node. For example, chess, suppose that a “node” is considered to be a legal position, then the average branching factor is estimated to be about 35. This means that, on average, a player has about 35 legal moves available at each turn. By comparison, the average branching factor for the game Go is 250!
Optimal status: it is not possible to perform better (some of these entries were solved by humans)
Super-human: performs better than all humans
Now, let’s talk about the machines.
The blog series will cover the following topics. Links to images are in original blogs.
The focus of the series is on some of the “firsts” in AI and games (and sometimes some of the predecessors of those programs), not on including *all* or *as many as possible* game programs.
How do we usually play this game? We do the following:
Consider all the legal moves that a player can make
Compute the new position resulting from each move
Evaluate to determine the next best move
Make that (best) move
Wait for opponent to make a move
Respond by repeating above steps
From this perspective, almost all chess computers must deal with these fundamental steps. And in doing that, a chess computer would have to address the following key problems:
Representing the “board”
Generating all legal next states
Evaluating a position
There were two main philosophical approaches to developing chess computers: emulation vs. engineering — should computers emulate human knowledge and decision-making or should computers improve search via brute force? Those focusing on the first approach would build programs that had a lot of chess knowledge and a relatively smaller focus on search. Those focusing on the engineering approach would focus on computational power, by using special-purpose hardware, and search innovations. We’ll see that the best chess computers used the second approach, but even they ended up using a lot of chess knowledge and sophisticated evaluation heuristics.
In the spring of 1770, Wolfgang von Kempelen created a sensation; he presented the world’s first ever chess-playing automaton, which he called the Automaton Chess-player, known in modern times as The Turk.
In early 1910s, Torres y Quevedo built an automaton called El Ajedrecista(The Chessplayer), which made its debut, at the University of Paris in 1914. It is considered to be one the world’s first computer games.
John von Neumann founded the field of game theory. In 1928 he proved the minimax theorem. This theorem states that in zero-sum games (i.e. if one player wins, then the other player loses) with perfect information (i.e. in which players know, at each time, all moves that have taken place so far), there is a pair of strategies for both players that allows each to minimize his maximum losses, hence the name minimax.
From 1940s to early 1950s, early pioneers focused on building machines that would play chess much like humans did, so early chess progress relied heavily on chess heuristics (rules of thumb) to choose the best moves. They emphasized emulation of human chess thought process because they believed teaching a machine how to mimic human thought would produce the best chess machines.
Starting in mid 1940s, scientists from different fields (mathematics, psychology, engineering, etc.) had started discussing the possibility of creating a machine that could think, a machine that could compete with or even surpass humans in pattern recognition, computation, problem solving and even language. Claude Shannon wrote the very first article ever published on programming a computer to play chess. He published a paper in Philosophical Magazine entitled Programming a computer to play chess.
Computing power was limited in the 1950s, so machines could only play at a very basic level. This is the period when researchers developed the fundamental techniques for evaluating chess positions and for searching possible moves (and opponent’s counter-moves). These ideas are still in use today.
In 1953, Alan Turing published an article on his chess program (Digital Computers Applied to Games) in the book Faster than Thought by B. Bowden. Shannon had not spoken about any particular program in his paper. It was Turing who wrote the first chess program. And he wrote it before computers even existed! He knew computers were coming and once they were powerful enough, they would be able to play chess. In 2012, Garry Kasparov played against Turochamp and defeated it in just 16 moves. Kasparov said (video), “I suppose you might call it primitive, but I would compare it to an early car — you might laugh at them but it is still an incredible achievement…
[Turing] wrote algorithms without having a computer — many young scientists would never believe that was possible. It was an outstanding accomplishment.”
The team that programmed MANIAC was led by Stanislaw Ulam (who invented nuclear pulse propulsion and designed the H-bomb with Edward Teller), Paul Stein, Mark Wells, James Kister, William Walden and John Pasta. Due to MANIAC’s limited memory, the program used a 6 × 6 chessboard and no bishops. MANIAC I performed a brute-force Shannon Type A strategy. It performed 11,000 operations per second, and had 2,400 vacuum tubes. It took 12 minutes to search a four moves depth (adding the two bishops would take three hours to search at the same depth).
Alex Bernstein, an IBM employee, created the first program that could play a full game of chess. He created it with his colleagues Michael Roberts, Thomas Arbucky and Martin Belsky, Bernstein at the Massachusetts Institute of Technology. The program ran on an IBM 704 and could perform 42,000 instructions per second. This was one of the last vacuum tube computers. It took about 8 minutes to make a move.
The Bernstein Chess Program used Shannon Type B (selective search) strategy.
In 1952, Arthur Samuel completed his first checkers program on IBM 701 — the first major commercial computer. By 1955, Samuel had done something groundbreaking; he had created a program that could learn — something that no one had done before — and it was demonstrated on television in 1956. Samuel had been thinking about machine learning since he joined IBM and wanted to focus on building programs that could learn to play the game of checkers. In 1959 he coined the term “machine learning”.
During World War II, Donald Michie, a British computer scientist, worked for the Government Code and Cypher School at Bletchley Park and helped break the German “Tunny” code. In 1960 he developed the Machine Educable Noughts And Crosses Engine (MENACE), one of the first programs capable of learning to play a perfect game of Tic-Tac-Toe. Computers were not readily available so he used 304 matchboxes all filled with colored beads in order to learn to play. Coming soon…
By the end of the 1960s, computer chess programs were good enough to occasionally beat against club-level or amateur players.
Mac Hack (also known as The Greenblatt Chess Program) is a knowledge-based chess program built by Richard Greenblatt in 1967. In the same year, Mac Hack VI became the first computer to play against humans under human tournament conditions. MacHack program was the “first widely distributed chess program”, running on many PDP machines. It became the first computer to reach the standard of average tournament players. It was demonstrably superior to all previous chess programs as well as most casual players. In 1965, Hubert Dreyfus said, “No chess program could play even amateur chess.” In 1967, Mac Hack VI beats Dreyfus.
In 1970s and1980s, the emphasis was on hardware speed. In the 1950s and 1960s, early pioneers had focused on chess heuristics (rules of thumb) to choose the best next moves. The programs in 1970s and 1980s also used some chess heuristics, but there was a much stronger focus on software improvements as well as use of faster and more specialized hardware. Customized hardware and software allowed programs to conduct much deeper searches of game trees (example: involving millions of chess positions), something humans did not (because they could not) do.
Cray Blitz, developed by Robert Hyatt, Harry L. Nelson, and Albert Gower, entered ACM’s North American Computer Chess Championship in 1976. It became a serious competitor in 1980 when it was moved to a Cray-1 supercomputer, becoming the first chess program to use such a powerful machine. This made it possible for Cray Blitz to adapt a mostly algorithmic and computational approach while still retaining most of its (extensive) chess knowledge.
BKG, a backgammon program created by Hans Berliner, was the first time that a machine had beaten a world champion in any game. And it did it with the slow(er) computers of those days. [Side note: Berliner developed the B* search algorithm for game tree searching.]
HiTech was a chess machine with special purpose hardware and software. It was built by Hans Berliner and others at CMU. Its custom hardware could analyze ~175,000 moves per second and it could execute a full-width depth-first search. It was one powerful machine. In 1985 it achieved a rating of 2530, becoming the first (and at the time only) machine to have a rating over 2400.
After Samuel’s work on checkers, there was a false impression that checkers was a “solved” game. As a result, researchers moved on to chess and mostly ignored checkers until Jonathan Schaeffer began working on Chinook in 1989. Schaeffer’s goal was to develop a program capable of defeating the best checkers player. The best player was Marion Tinsley. During a match, Chinook won a game against Tinsley, to which Schaeffer responded,
“We’re still members of the human race and Chinook defeating Tinsley in a single game means that it will only be a matter of time before computers will be supreme in checkers, and eventually in other games like chess.”
Read moreto see how Chinook vs. Tinsley played out.
In the 1990s,chess programs began challenging International Chess masters and later Grandmasters. A specialized chess machine, named Deep Blue, would end up beating Garry Kasparov, the best human chess player. We also saw successful applications of reinforcement learning (something AlphaGo would do years later).
TD-Gammon was backgammon program developed in 1992 by Gerald Tesauro of IBM. TD-Gammon applied a reinforcement learning, which goes back to Samuel’s Checkers program (later we’ll see so does alphaGo). Tesauro said that TD-Gammon’s self-teaching methodology resulted in a surprisingly strong program.TD-Gammon 3.0 (1997) used a selective 3-ply search and was already at, or very near, the level of the best human players in the world. In the long run, TD-Gammon was expected to play even better than grandmasters because those grandmasters were only human. And humans get tired. Humans have biases, but TD-Gammon didn’t. TD-Gammon played differently than humans did — a theme we will notice more and more. Read more.
This one is long (and super interesting). Definitely read it.
Deep Blue was only a two-week old baby when it faced Garry Kasparov in 1996. Hsu, one of its creators said, “Would it be the baby Hercules that strangled the two serpents send by the Goddess Hera? Or were we sending a helpless baby up as a tribute to placate the sea monster Cetus, but without the aid of Perseus? We were afraid it would be the latter.” The first game it ever played against Kasparov, it won — prompting Kasparov to question himself and asking, “…what if this thing is invincible?” It wouldn’t be invincible and Kasparov would beat it 4–2. This match was much closer than most people think (Read more).
After the match Kasparov said (about Deep Blue),
“I could feel — I could smell — a new kind of intelligence across the table.”
There would be a rematch in 1997. This time the Deep Blue team has significantly improved their machine (read about the improvements, the system architecture, the search strategies, the chess chips, etc.) This time Deep Blue plays a game no one expected and beats Garry Kasparov. Kasparov implies IBM cheated (IBM didn’t cheat) because Deep Blue’s play had a component of “humanness”. After this match, Kasparov said,
“I was not in the mood of playing at all..I’m a human being. When I see something that is well beyond my understanding, I’m afraid.’’
This is the match everyone had an opinion about — was Deep Blue intelligent? Who was a better player? Did Deep Blue think? What does such a victory say about us? Until Deep Blue, humans were winning at chess. Machines really couldn’t beat the best humans — not even close. But then Deep Blue won. And soon so did the other computers and they have been beating us ever since. This massive growth is their identity — no matter what our rate of improvement, once machines begin to improve, their learning and progress ends up being measured exponentially. And ours doesn’t. Read more to get the details of how this machine was built, who was involved, the type of effort the team needed to put into the project, how Kasparov dealt with the defeat and how it became a significant marker in the history of AI.
People believed Kasparov was still a better player, but his emotions got in the way. Either way, one of the biggest takeaways from this match was that we had collectively underestimated both the physiological and psychological aspects of the match.
Our emotions, fears, desires, and doubts had a way of getting the best of us…And this is a uniquely human problem, one our machine opponents do not worry about.
In 2007, the makers of Chinook published a paper in the journal Science announcing that Chinook had completely solved Checkers: the program could no longer be defeated by any, human or otherwise. Jonathan Schaeffer and his team had been working to solve the checkers problem since 1989. The article stated, “…checkers is now solved: Perfect play by both sides leads to a draw. This is the most challenging popular game to be solved to date, roughly one million times as complex as Connect Four.” Checkers is the largest game that has been solved to date, with a search space of 5×10^20. “The number of calculations involved was 10^14, which were done over a period of 18 years. The process involved from 200 desktop computers at its peak down to around 50”. Coming Soon
In 2011, IBM developed Watson, a question-answering system capable of answering questions posed in natural language, competed on Jeopardy! against legendary champions Brad Rutter and Ken Jennings and won. Coming soon
In 2015 Google DeepMind’s AlphaGo defeated 3 time European Go champion 2 dan professional Fan Hui 5–0. In 2016 it defeated a 9 dan professional Lee Sedol 4–1. Before the match with AlphaGo, Lee Sedol was confident it would be an easy win and predicted a 5–0 or 4–1 victory. AlphaGo stunned the world because Go had previously been regarded as a very hard problem in machine learning — everyone thought it was out of reach for the technology of that time. In 2017 DeepMind’s AlphaGo Zero beat AlphaGo by 100 games to 0. It had learned by playing games against itself, starting from completely random play. (We’ve seen this before in Samuel’s checkers and Tesauro’s TD-Gammon.) Coming soon
AI has had successes in perfect information games, but poker is not a perfect information game — a player has private information that others don’t. In a 120,000-hand competition, Libratus defeated 4 top professionals in heads-up no-limit Texas hold’em. (No-limit Texas hold ’em is the most popular form of poker.)
Optimal: it is not possible to perform better (some of these entries were solved by humans)
Super-human: performs better than all humans
High-human: performs better than most humans
In 2016, Katja Grace and others of the Future of Humanity Institute conducted an expert poll , where they asked experts to give median estimates for certain AI tasks. Here are some results that are relevant to AI and games:
“What I learned from my own experience is that we must face our fears if we want to get the most out of our technology, and we must conquer those fears if we want to get the best out of our humanity. While licking my wounds, I got a lot of inspiration from my battles against Deep Blue. As the old Russian saying goes, if you can’t beat them, join them. Then I thought, what if I could play with a computer — together with a computer at my side, combining our strengths, human intuition plus machine’s calculation, human strategy, machine tactics, human experience, machine’s memory. Could it be the perfect game ever played? But unlike in the past, when machines replaced farm animals, manual labor, now they are coming after people with college degrees and political influence. And as someone who fought machines and lost, I am here to tell you this is excellent, excellent news. Eventually, every profession will have to feel these pressures or else it will mean humanity has ceased to make progress. We don’t get to choose when and where technological progress stops.
We cannot slow down. In fact, we have to speed up. Our technology excels at removing difficulties and uncertainties from our lives, and so we must seek out ever more difficult, ever more uncertain challenges. Machines have calculations. We have understanding. Machines have instructions. We have purpose. Machines have objectivity. We have passion. We should not worry about what our machines can do today. Instead, we should worry about what they still cannot do today, because we will need the help of the new, intelligent machines to turn our grandest dreams into reality. And if we fail, if we fail, it’s not because our machines are too intelligent, or not intelligent enough. If we fail, it’s because we grew complacent and limited our ambitions. Our humanity is not defined by any skill, like swinging a hammer or even playing chess.There’s one thing only a human can do. That’s dream. So let us dream big.”