"In March 1998, the New York Times sponsored an exhibition match between MAVEN and a team consisting of world champion Joel Sherman and runner-up Matt Graham. It is not clear whether the collaboration helped or hindered the human side, but the computer won convincingly by a score of six wins to three. The result was not an anomaly. In July 1998, MAVEN played another exhibition match against Adam Logan (at AAAI-98), scoring nine wins to five."
– from A Gamut of Games, Jonathan Schaeffer, AI Magazine 22(3): Fall 2001, 29-46.
You can learn a lot about a person from the way they play Scrabble. Do they show off their SAT vocabulary or only know dirty words? Are they rule-sergeants or are they so competitive that they will stop at nothing to beat someone who is half their age? It seems his Scrabble strategy involves aggressive rule bending in order to win a game against a high school-age opponent. SEE ALSO: After losing trust of its users, Facebook assigns them a'trustworthiness' score This little Zuckerian anecdote comes to us from an extensive New Yorker profile about the Facebook CEO's approach toward the myriad problems currently facing the social network, and whether he's equipped to solve them.
Look, there's plenty of boring industrial robots on the floor of the Las Vegas convention center for CES. But I've got to hand it to the Industrial Technology Research Institute (ITRI) -- it really knows how to make a demo fun and interactive. The company combined a number of its technologies into a robot that is able to sit and play Scrabble against a human opponent ... and win.
Beating people at Scrabble is already no contest for computer programs, which can easily memorise entire dictionaries. Now a Scrabble-playing program has gone one better by playing dirty. Developed by Eyal Amir and Mark Richards at the University of Illinois, Urbana-Champaign, the program is able to predict which letter tiles other players hold, and use this information to choose moves which block a high-scoring word that an opponent might otherwise have played. This aggressive gaming style gives it the edge over previous Scrabble programs, which focus solely on maximising their own scores. To predict what tiles other players hold, Amir and Richards's program begins by eliminating those tiles that have already been played.
In Shannon's time, it would have seemed Around this time, Arthur Samuel began work the capabilities of computational intelligence. By 1958, Alan Newell and Herb Simon the game world with the real world--the game had begun their investigations into chess, of life--where the rules often change, the which eventually led to fundamental results scope of the problem is almost limitless, and for AI and cognitive science (Newell, Shaw, and the participants interact in an infinite number Simon 1958). An impressive lineup to say the of ways. Games can be a microcosm of the real least! Indeed, one of the early goals of AI was to and chess programs could play at a build a program capable of defeating the level comparable to the human world champion. This These remarkable accomplishments are the challenge proved to be more difficult than was result of a better understanding of the anticipated; the AI literature is replete with problems being solved, major algorithmic optimistic predictions. It eventually took insights, and tremendous advances in hardware almost 50 years to complete the task--a technology. The work on computer remarkably short time when one considers the games has been one of the most successful and software and hardware advances needed to visible results of AI research. The results are truly of the progress in building a world-class amazing. Even though there is an exponential program for the game is given, along with a difference between the best case and the brief description of the strongest program. The histories are necessarily case (Plaat et al. 1996). Games reports the past successes where computers realizing the lineage of the ideas.