Author: Bob Durrett
Date: 19:34:36 11/08/00
Chess engines [or programs] which can learn might be compared with regard to how fast and how well they learn. One could define a chess-playing program's I.Q. by creating a scale with the "average" learning computer arbitrarily assigned an I.Q. of 100 and the smartest [fastest &/or best learning] currently available program assigned an I.Q. of, say, 150. Similarly, one which learns poorly could be assigned an I.Q. of, say, 50. A program without any learning ability at all would be said to have an I.Q. of zero. The rest of the programs could be placed on the scale in some manner which made sense. Chess-playing programs which can learn might differ in various ways, which might make the definition of I.Q. somewhat awkward. For example, one program might only be able to improve it's opening book. Another might be able to increase it's strength in the middlegame, assuming that it plays worthy opponents who are strong enough to teach the program something of value. Similarly in the endgame. This raises several questions: (1) What are the I.Q.s of popular chess engines [or programs]? In what ways do they differ with respect to their learning abilities? (2) Which engines/programs should be rated the highest with respect to it's learning ability? [possibly broken down by phase of the game in which learning can occur] (3) Are there any practical limits to how smart [high I.Q] chess engines/programs can be made? What sets those limits? (4) If an off-the-shelf "high-I.Q." chess engine can be educated, then why not deliberately educate it prior to delivery to the customers? The only requirement seems to be the availability of qualified teachers. (5) Are there any practical limitations on how well educated a chess engine/program can be? [Do their "brains" get "filled up"?] Could you have a "PhD" engine who graduated, after several years of "study" from a "college" where all of the "professors" were top GMs?
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