To design an algorithm for playing the game of chess has been one of the challenges that has attracted the attention of many mathematicians and computer scientists. The sheer number of combinatorial possibilities make it hard to predict the result for both humans and computers alike. There have been many highly publicized games pitting humans against the (super) computers in the ’90s and ’00s, such as the Deep Blue vs. Kasparov one.
It was around the same time that I was starting out with chess and was interested in learning how to play better. My father had gifted me a copy of a computer game called Maurice Ashley Teaches Chess. It included playing strategies, past-game analysis and video coaching by the chess grandmaster Maurice Ashley. It also had a practice mode where you could compete and play against the computer. I didn’t end up being a good chess player but if my memory serves me right, it did not take me long to start beating the in-game AI. But things have changed a lot since then. Computers are not only faster and more powerful now (to explore more number of moves) but are also equipped with better algorithms to evaluate a decision. Let’s compare excerpts from the introductory chapters from two of my textbooks:
From "Cognitive Psychology" (Medin et.al., 2004):
The number of ways in which the first 10 moves can be played is on the order of billions and there are more possible sequences for the game than there are atoms in the universe! Obviously neither humans nor machines can determine the best moves by considering all the possibilities. In fact, grandmaster chess players typically report that they consider only a handful of the possible moves and “look ahead” for only a few moves. In contrast, chess computers are capable of examining more than 2,000,000 potential moves per second and can search quite a few moves ahead. The amazing thing is that the best grandmasters (as of this writing) are still competitive with the best computers.
Now consider, "Artificial Intelligence: A Modern Approach (3rd Edition)" (Russell et.al., 2010):
IBM’s DEEP BLUE became the first computer program to defeat the world champion in a chess match when it bested Garry Kasparov by a score of 3.5 to 2.5 in an exhibition match (Goodman and Keene, 1997). Kasparov said that he felt a “new kind of intelligence” across the board from him. Newsweek magazine described the match as “The brain’s last stand.” The value of IBM’s stock increased by $18 billion. Human champions studied Kasparov’s loss and were able to draw a few matches in subsequent years, but the most recent human-computer matches have been won convincingly by the computer.
So, what happened in the six year gap between the publishing of these books? It turns out that there has indeed been such a shift in the recent years. The computers’ superior performance stats can be seen on this Wikipedia entry. We have come a long way since the Kasparov vs. Deep Blue matches due the the advancements in both hardware and AI algorithms. Computers have now started not only wining but dominating in the human-computer chess matches so much so that even mobile phones running slower hardware are reaching Grandmaster levels. Guess, time’s right for switching to new board games! Btw, Checkers is a solved problem since 2007: http://www.sciencemag.org/content/317/5844/1518.full! It will end up in a draw (they have a computational proof of that) if both players use the perfect strategies, i.e. the one that never loses.
Image Credits: en:User:Cburnett / Wikimedia Commons / CC-BY-SA-3.0 / GFDL
- Russell et.al. (2010), Artificial Intelligence: A Modern Approach (3rd Edition), 49. Prentice Hall. Available: http://www.amazon.com/Artificial-Intelligence-Modern-Approach-Edition/dp/0136042597.
- Medin et.al. (2004), Cognitive Psychology. Wiley. Available: http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0471458201.