I have learned..
23 Oct '05 18:17... something new today here that might have an impact on one of my pet projects at home. I discovered in the "birthdays problem" thread that I have been thinking very backwards when considering probability problems.
I have been trying to solve one of these for ten years (off and on).
The problem concerns predicting the outcome of a game of chance that can be described (haltingly) like this:
Two contestants A and B are defined by 4 variables each.
"Life" (Any value >0),"Attack" (0-100),"Defence" (0-100) and
"Damage" (any value >0). ("life" is most commonly much larger than "damage"😉
Chance decides who begins.
They then take turns.
One players Defence is deducted from the others Attack (P1).
A random number (R1) between 0-100 is chosen. If P1 > R1 or R1=0 a second random number is chosen [0-Damage] and that number is deducted from the Defending players Life.
Then roles reverse and the attacking player defends.
The above is repeated until one player has no life left.
I am currently trying to evolve a neural net using a genetic algorithm to
predict the probable outcome given only the eight (normalized) variables.
The net is placing the outcome in four categories 0-25% 25-50% 50-75% and 75-100%. It seems however I have some trouble getting the net to evolve past 90.xx% fitness.
Is there a way (simpler than what i am doing) to calculate the correct outcome?
I have been trying to solve one of these for ten years (off and on).
The problem concerns predicting the outcome of a game of chance that can be described (haltingly) like this:
Two contestants A and B are defined by 4 variables each.
"Life" (Any value >0),"Attack" (0-100),"Defence" (0-100) and
"Damage" (any value >0). ("life" is most commonly much larger than "damage"😉
Chance decides who begins.
They then take turns.
One players Defence is deducted from the others Attack (P1).
A random number (R1) between 0-100 is chosen. If P1 > R1 or R1=0 a second random number is chosen [0-Damage] and that number is deducted from the Defending players Life.
Then roles reverse and the attacking player defends.
The above is repeated until one player has no life left.
I am currently trying to evolve a neural net using a genetic algorithm to
predict the probable outcome given only the eight (normalized) variables.
The net is placing the outcome in four categories 0-25% 25-50% 50-75% and 75-100%. It seems however I have some trouble getting the net to evolve past 90.xx% fitness.
Is there a way (simpler than what i am doing) to calculate the correct outcome?