One of my long-term goals is to write a chess program that plays more like a lower-level human. Every program I've played seems to have the same pattern: play like a master for ten moves, then make an insultingly obvious blunder to let me catch up. Lather, rinse, repeat. It's neither realistic nor fun. Might as well play with a handicap.
Real players at each level tend to have characteristic weaknesses that are expressible in terms of the same factors used in a program's evaluation function. Consider some of the following, which players at a certain level will exhibit and then get past as they improve.
* Bringing the queen out too early.
* Missing pins and discoveries.
* Failing to contest the center.
* Creating bad bishops.
* Bad pawn structure.
* Failing to use the king effectively in the endgame.
These are all quantifiable. They could all be used to create a more realistic and satisfying opponent at 1200, 1500, 1800, etc. All it takes is some basic machine learning applied to a corpus of lower-level games, and a way to plug the discovered patterns into the playing engine.
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One of my long-term goals is to write a chess program that plays more like a lower-level human. Every program I've played seems to have the same pattern: play like a master for ten moves, then make an insultingly obvious blunder to let me catch up. Lather, rinse, repeat. It's neither realistic nor fun. Might as well play with a handicap.
Real players at each level tend to have characteristic weaknesses that are expressible in terms of the same factors used in a program's evaluation function. Consider some of the following, which players at a certain level will exhibit and then get past as they improve.
* Bringing the queen out too early.
* Missing pins and discoveries.
* Failing to contest the center.
* Creating bad bishops.
* Bad pawn structure.
* Failing to use the king effectively in the endgame.
These are all quantifiable. They could all be used to create a more realistic and satisfying opponent at 1200, 1500, 1800, etc. All it takes is some basic machine learning applied to a corpus of lower-level games, and a way to plug the discovered patterns into the playing engine.