Tim Ferriss of “Four Hour Work Week / Body / Chef” fame has championed the idea of Minimum Effective Dose. This is simply the smallest does which will have the desired outcome. In the context of computer chess evaluation I think it’s interesting.
I would like to try to create an automatic parameter tuning framework for the evaluation function using Probability Based Incremental Learning. Thomas Petzke is doing some cool stuff in this area. However the more parameters I have the more difficult it will be to tune. So my aim is to create a Minimum Effective Evaluation. Of course over time I’ll add to the evaluation but only when improvements are significant and proven (at least that’s the intention).
To this end I’ve been going over some articles which I have previously saved to Evernote. This one from Ed Schroeder is of particular interest – The Value of an Evaluation Function. Ed shows the relative importance of each evaluation term. In summary it would seem the high level order of importance is as follows:
- Passed Pawn
- Piece Square Tables
- King Safety
- Minor Piece “Stuff”
- Pawn Evaluation
I’m going to use this to prioritize my effort.