All of the four parameters (mentioned in my last post) started to zone in on the values very well. For example the first parameter looks like this:
The mean value was -370 which can clearly be seen in the plot.
So on to all of the results and some comparisons (sanity checks I guess). These were the resulting values (meaning of these explained in my last post):
- MOBILITY_SAFE_MULTI = -370
- MOBILITY_UNSAFE_MULTI = 783
- MOBILITY_ONE_TRAPPED_MULTI = 767
- MOBILITY_ZERO_TRAPPED_MULTI = 1343
I'll do four different examples.
- Average piece - a piece with 4 safe squares and 1 unsafe
- Good piece - a piece with 12 safe squares and 3 unsafe
- Bad piece - a piece on the fourth rank with 1 safe square and 1 unsafe
- Trapped piece - a piece on the fourth rank with 0 safe squares and 1 unsafe
So comparisons (using the examples above):
- 4*2+5 = 13 (before tuning)
-370*4/100+783*5/100 = 25 (after tuning)
- 12*2+15 = 39
-370*12/100+783*15/100 = 73
- 1*2+1-4*5/2 = -7
-370*1/100+783*2/100-767*4/100 = -18
- 0*2+1-4*5 = -19
-370*0/100+783*1/100-1343*4/100 = -46
So very reasonable numbers, just a bit higher in all directions just as I suspected (always nice when you have a guess and testing confirms it).
First interesting thing is the negative safe square number. I'm sure it's not trying to penalize safe squares, but rather put all bonus in the total squares (as those include safe squares as well), meaning safe vs unsafe squares is really not that important.
Second thing is my apparently good guestimate of giving a piece with one square half the penalty of a piece with zero squares. Which the tuning seems to confirm by almost doubling the factor (767 to 1343).
Time to run a test with all these new values. Will be very interesting.