Reply
 
LinkBack Thread Tools Display Modes
  #1   Report Post  
Old November 24th 03, 02:54 AM
conan
 
Posts: n/a
Default Fritz 8 - Questions about learning

I am a beginner and have been playing "Rated" games agst Fritz8, i.e where
you cant take back moves. It helps me keep a good track of how i am doing,
and i can always go back and try a different line when the game is over, it
even remembers the times!

When i first started playing rated i set the "playing strength" slider all
the way to the left. It was 1540 originaly but after a few games the minimum
value seems to be higher (1590). All this without transfering the book on
the hard disk, i.e no learning. Obviously the computer must somehow keep
track of how well i am doing and incresed its minimum strength.

So my first question is what does the strength relate to, in laymans terms?
It cant be associated with the tree, thats different. It must have something
to do with when the engine goes 'out of book'. So how does it do it? does it
literarly slow itself down by counting from 0 to a gazillion? does it adjust
the search depth? Anyway...lets move on

So i have played ard 200 games, all kept in the autosave database. I have
found a couple of winning "sequences" which help me keep my win-loss
averages between 1-2 and 1-3. And this is where i am troubled. The whole
point is to learn how to play. So if i find a winning sequence its almost
like cheating. I do think there is value in going through the sequence again
because i remember it better, and i expect that its the same in real life,
as you can beat different people the same way as well as make the same
mistakes again. But obviously, after 10 identical games, further repetitions
will make me overconfident, ignoring different lines of play. This got me to
think about transfering the opening book to the hd and enabling the learning
function.

Having read the manual i realise that i have several options and
....questions. First of all what is the tournament option. Are there moves
that have been excluded from tournaments? What are they?

An intersting option is that it looks like i can adjust the weights based on
the games that i have played so far. That is a good idea although i am
worried that the weights are going to be overadjusted because of the
repetitive wining sequences, which the engine would not have gone through if
it was actual learning while playing each of the 200 games sequentialy (as
opposed to learning after 200 games played with no adjustment, and then
learning). I suppose i can set a low value on the weights for this and
adjust it after its done reading the database. But i am confused about the
weights too.

It seems there are three parameters
(1) variety of play
(2) influence of learn value
(3) learning strength

Having toyed with AI in college (perhaps this is where my troubles with this
come from) i would imagine that the adjustment that goes on, looks something
like

new prob = old prob + influence * strength

Several questions here. First it seems that influence and strength are
interchangable, not only from the equation above (which is probably
oversimplified to wrong) but also from how the are explained in the manual.

Also, variety is also a bit of a mystery. It seems that what it does, is
scew the distribution of moves, i.e greatest variety = uniform distribution.
Although i guess this defeates the whole learning process since uniform
distribution means all moves have equal probability, it might be used for
revising. I.e. by setting variety towards the maximum, the engine is forced
to play lines for which it has a losing record more often, thus testing the
players memory!?

Another question is how do the adjustments backpropagate, i.e if the engine
loses game does the whole line of play get a "bad mark"? Presumably if the
last move of a game was a blunder, this does not necessarily mean that the
previous moves where also bad.

So maybe the learning strenth is constant throughout the tree but the
influence varies according to how high up we are in the tree. After all if
playing white, the engine loses a game it has opened with e4, this does not
mean that e4 is a bad move. So maybe the adjustment looks like

new prob = old prob + F(influence) * strength

where F(x) is a function which is tree (depth) related, for example

f=(move no./total moves in the game)*influence

so that the 1st move gets (1/total moves)*influence of an adjustment, which
is a lot less than the checkmate move which gets (total moves/total
moves)*influence, a bigger adjustment.

Does this make sense?

So how do different settings affect the engine and more importantly the
player? I am really hoping that someone has played around with these and can
give some insight, or know outright.

A low strength value would mean that it would take a lot of games for the
engine to make up its mind about anything, be it a move, or a line of play.
A high strength would mean that the engine would not try again a move which
ended in a loss.

A low influence would mean that the engine would still play moves that later
ended up in a loss, whereas high influence runs the risk of discarding whole
trees.

Still this is not that clear. After all, what the engine does during play,
is search. So do these values also affect which moves it searches? Possibly.
And what does increasing the playing strength in rated games do in relation
to the weights?

If anyone knows or has thought about any of these....fire away


  #2   Report Post  
Old November 24th 03, 04:56 AM
marc margolies
 
Posts: n/a
Default Fritz 8 - Questions about learning

i am guessing that you are confusing tow learnings, yours and the machines.
when the machine learns that means it remebers how it lost in some such
variation or standard postion so it avoids it, or may be changes its own
move there. that is what a book learning option in fritz does.
This has little to do with changes in your own rating and the subsequent
changes a program might make to its playing style in order to give you an
enjoyable game with more winning chances for you.
"conan" wrote in message
...
I am a beginner and have been playing "Rated" games agst Fritz8, i.e where
you cant take back moves. It helps me keep a good track of how i am doing,
and i can always go back and try a different line when the game is over,

it
even remembers the times!

When i first started playing rated i set the "playing strength" slider

all
the way to the left. It was 1540 originaly but after a few games the

minimum
value seems to be higher (1590). All this without transfering the book on
the hard disk, i.e no learning. Obviously the computer must somehow keep
track of how well i am doing and incresed its minimum strength.

So my first question is what does the strength relate to, in laymans

terms?
It cant be associated with the tree, thats different. It must have

something
to do with when the engine goes 'out of book'. So how does it do it? does

it
literarly slow itself down by counting from 0 to a gazillion? does it

adjust
the search depth? Anyway...lets move on

So i have played ard 200 games, all kept in the autosave database. I have
found a couple of winning "sequences" which help me keep my win-loss
averages between 1-2 and 1-3. And this is where i am troubled. The whole
point is to learn how to play. So if i find a winning sequence its almost
like cheating. I do think there is value in going through the sequence

again
because i remember it better, and i expect that its the same in real life,
as you can beat different people the same way as well as make the same
mistakes again. But obviously, after 10 identical games, further

repetitions
will make me overconfident, ignoring different lines of play. This got me

to
think about transfering the opening book to the hd and enabling the

learning
function.

Having read the manual i realise that i have several options and
...questions. First of all what is the tournament option. Are there moves
that have been excluded from tournaments? What are they?

An intersting option is that it looks like i can adjust the weights based

on
the games that i have played so far. That is a good idea although i am
worried that the weights are going to be overadjusted because of the
repetitive wining sequences, which the engine would not have gone through

if
it was actual learning while playing each of the 200 games sequentialy (as
opposed to learning after 200 games played with no adjustment, and then
learning). I suppose i can set a low value on the weights for this and
adjust it after its done reading the database. But i am confused about the
weights too.

It seems there are three parameters
(1) variety of play
(2) influence of learn value
(3) learning strength

Having toyed with AI in college (perhaps this is where my troubles with

this
come from) i would imagine that the adjustment that goes on, looks

something
like

new prob = old prob + influence * strength

Several questions here. First it seems that influence and strength are
interchangable, not only from the equation above (which is probably
oversimplified to wrong) but also from how the are explained in the

manual.

Also, variety is also a bit of a mystery. It seems that what it does, is
scew the distribution of moves, i.e greatest variety = uniform

distribution.
Although i guess this defeates the whole learning process since uniform
distribution means all moves have equal probability, it might be used for
revising. I.e. by setting variety towards the maximum, the engine is

forced
to play lines for which it has a losing record more often, thus testing

the
players memory!?

Another question is how do the adjustments backpropagate, i.e if the

engine
loses game does the whole line of play get a "bad mark"? Presumably if the
last move of a game was a blunder, this does not necessarily mean that the
previous moves where also bad.

So maybe the learning strenth is constant throughout the tree but the
influence varies according to how high up we are in the tree. After all if
playing white, the engine loses a game it has opened with e4, this does

not
mean that e4 is a bad move. So maybe the adjustment looks like

new prob = old prob + F(influence) * strength

where F(x) is a function which is tree (depth) related, for example

f=(move no./total moves in the game)*influence

so that the 1st move gets (1/total moves)*influence of an adjustment,

which
is a lot less than the checkmate move which gets (total moves/total
moves)*influence, a bigger adjustment.

Does this make sense?

So how do different settings affect the engine and more importantly the
player? I am really hoping that someone has played around with these and

can
give some insight, or know outright.

A low strength value would mean that it would take a lot of games for the
engine to make up its mind about anything, be it a move, or a line of

play.
A high strength would mean that the engine would not try again a move

which
ended in a loss.

A low influence would mean that the engine would still play moves that

later
ended up in a loss, whereas high influence runs the risk of discarding

whole
trees.

Still this is not that clear. After all, what the engine does during

play,
is search. So do these values also affect which moves it searches?

Possibly.
And what does increasing the playing strength in rated games do in

relation
to the weights?

If anyone knows or has thought about any of these....fire away




Reply
Thread Tools
Display Modes

Posting Rules

Smilies are On
[IMG] code is On
HTML code is Off
Trackbacks are On
Pingbacks are On
Refbacks are On



All times are GMT +1. The time now is 06:23 PM.

Powered by vBulletin® Copyright ©2000 - 2019, Jelsoft Enterprises Ltd.
Copyright 2004-2019 ChessBanter.
The comments are property of their posters.
 

About Us

"It's about Chess"

 

Copyright © 2017