Why is the recent ‘Go’ victory so important for AI? (Part 1)

Anyone who has seen any news in the last few days will know that a computer has for the first time beaten a top human player at the ancient Chinese game of ‘Go’.  In fact, at the time of writing, the AI (let’s call it by its name:  AlphaGo) has beaten its opponent 3 times, and the human (Lee So-dol) has won one – the fifth in the series takes place shortly.  But why is this such important news for AI?

After all, AI has been beating top grand-masters at Chess for a while now – Gary Kasparov was beaten by a computer in 1997, and although the exact ‘fairness’ of those matches has been questioned by some, it’s certainly been the case since about 2006 that a ‘commercially available’ computer running standard software can beat any human player on the planet.

So why is ‘Go’ so different?  In many ways, it’s a very similar game.  It’s ‘zero-sum’ (meaning one player’s loss exactly matches the other players gain), deterministic (meaning there is no random element to the game), partisan (meaning all moves are available to both players), and ‘perfect-information’ (meaning both players can see the whole game state – there are no hidden elements or information).   Just like Chess.

From an AI point of view, two things make ‘Go’ vastly more difficult than Chess.

Firstly, the board is a lot bigger (19×19), meaning that the average number of legal moves per turn is around 200 (compared to an average of 37 for chess).  This means that the ‘combinatorial explosion’ (which makes chess difficult enough) is much worse for ‘Go’:  to calculate the next 4 moves (2 each for each player) would need 320,000,000,000 board positions to be analysed – and looking ahead 2 moves each would give a pathetically weak game.

The second factor is that for Chess, analysing the ‘strength’ of a board position is fairly easy.  The material ‘pieces’ each player owns are all worth something that can be approximated with a simple scoring system, and that can be made more elaborate with some simple extra strategic rules (knights are more valuable near the centre, pawns are best arranged in diagonals, etc).  But for ‘Go’, a simple ‘piece counting’ system is nothing like a useful enough indicator of the advantage a player has in the game, and no ‘simple rules’ can be written which help.

Instead, good human players (and even relative amateurs) can assess a board position, more or less just by using their intuition, and that intuition is where a lot of the best play comes from.  Computers, of course, are not well known for their use of ‘intuition’.

I’ll write more about the approach ‘AlphaGo’ used – and why this has wider implications for AI in general – in a follow-up article in the next few days.

Chess – the classic AI problem

To settle a pub argument that took place around 20 years ago (I’ve been busy, OK?!) about artificial intelligence and emergent behaviour, and because I have some work coming up in this area, I have finally got around to writing a simple chess engine.

In terms of the pub argument, it has immediately proved that (a) yes, a chess engine can be very easily capable of beating the person who wrote it, and (b) even though it has no built-in knowledge of tactics such as forks, skewers or discovered attacks, it can and will use all such techniques during a game to very good effect. In other words, that behaviour ’emerges’ from the raw computation.

Neither of those things will come as any surprise to anyone who understands anything
about AI, of course. But it was fun to write, and it’s actually quite fun to play against, but also fun to play *with*. For example, it’s interesting to see how it chooses different moves depending on how many moves ahead it’s allowed to look.

When away from the field of computer vision, the speed of a modern computer is astounding. My code is currently very unoptimised, and was written mostly for ease of writing rather than with performance in mind – and yet, on a normal desktop PC, running on one core only, it is capable of generating, and analysing, approximately one million moves (board positions) per second.

Of course, chess is a famous example of a problem with a ‘combinatorial explosion’, meaning that for even relatively small numbers of moves to look ahead, the number of possible board positions rapidly becomes fantastically large. At 5 moves ahead, it is looking at around 100 million board positions – suddenly a millions moves per second doesn’t seem so much.

I am currently playing it ‘against’ another computer chess game, which after a few games will provide an estimated ELO score – I’ll report back in a day or two as to the results of that.

The current algorithm is pure ‘Minimax’, with no modifications.  It has no opening book or endgame database, so the play is a bit ragged at those stages. The first couple of moves are fairly random, but as the two sides ‘engage’ it starts making proper moves. By the end-game (at least when playing against me) it’s usually got enough of an advantage to be fairly decisive – it usually manages a check-mate during the ‘main’ part of the game, rather than waiting for a typical ‘endgame’ situation where we’ve only got 2-3 pieces each.

When time allows, I have other plans for this – after building in simple alpha-beta pruning to speed it up, I want to start to work on strategies – but from an AI point of view, I want it to be able to learn strategies itself, rather than be pre-programmed with them. I have some ideas in mind, and it should be interesting.