On human and computer intelligence in chess (with solution!)

In March there was an international furore over a chess position, published by mathematics professor Sir Roger Penrose, who gained world-wide renown in 1988 by working out black hole singularities together with Stephen Hawking. The chess problem was devised to defeat an artificially intelligent (AI) computer but be solvable for humans: “We plugged it into Fritz, the standard practice computer for chess players, which did three-quarters of a billion calculations, 20 moves ahead," explained James Tagg Co-Founder and Director of the Penrose Institute. "It says that one side or the other wins. But," Tagg continued, "the answer that it gives is wrong."

We reported on the Penrose problem, and confirmed that chess engines give a very high evaluation in favour of Black, who has a huge material advantage. But the pieces are all constricted and cannot be used against the lone white king, supported by four pawns. Humans will recognize that it is trivially easy for White to hold the draw, but computers display a +25-30 pawn advantage for Black – while defending perfectly with white! Not the best example of the difference between human and machine thinking.

I described a similar situation that occurred in a well-publicized game between IM David Levy and the computer program CHESS 4.8 in 1979. Levy had a queen and the computer a c-pawn on the seventh rank. It thought it was completely lost but defended the position perfectly to a draw.

In the first article I gave an example of a more relevant position, one in which there is a theoretical draw that humans can find, but where computer will play the wrong move and actually lose:

Here the correct first move for White, who is at an overwhelming material disadvantage, is to sacrifice even more material. It is the only way to secure a draw: White must play 1.Ba4+!, and after 1...Kxa4 play b3+, c4+, d5+, e6+ and finally f5 to completely lock up the position. This is a much more relevant test, as chess engines, playing the white side, will actually select the wrong strategy and lose the game – while in the Penrose position computers think that White is losing, but hold the draw without any problem.

After the above articles appeared I received a number of very interesting emails, which I intend to share with our readers over the next few weeks. They have to do mainly with the fortress theme and the way it is handled by computers. Today I will start with a communication from Miguel Illescas, a top Spanish GM and trained computer scientist, who worked for the Deep Blue team that beat World Champion Garry Kasparov in 1997. Miguel wrote:

"Several years ago I shared with my colleague [from the Deep Blue team] Murray Campbell an idea to make the computer 'understand' a fortress, but nobody has done it so far (I think). It looks simple to me: if the evaluation of several best moves is exactly the same and it stays stable it means there is no win. If that is true it shows computer programmers are not interested to solve something which is so unlikely to happen – or they are simply lazy!"

The Illescas Challenge

Together with this message I received a challenge which I pass on to our readers:

You are invited to analyse the position here on our news page, or show it to your favourite chess engine (FEN: 1r6/1n1R1b2/8/1p1p3k/pPpPp1p1/2P1P3/P2K1PP1/8 w - - 0 1) and solve it with machine assistance. The solution and analysis will be added to this article in a few days – after which I will share the thoughts and ideas of other AI scientist on chess positions that are difficult for computers to comprehend.

Solution