Parameter learning problem

[Niels Taatgen]

I want to thank everyone for their contributions to the discussion. As I
now look at it, there are two problems to be solved:
- The current parameter learning mechanism does not take into account
the fact that after a failed production the goal may still be reached.
- How can the "feeling of knowing" be modeled in ACT-R and applied to
the compute/retrieve choice.

With respect to the first problem, I wondered why we make destinctions
between q and r, and a and b? Why not estimate P and C directly from
experience, and use these as production parameters? Since q and r are
always multiplied, and a and b are always added, I cannot think of a
good reason to estimate them seperately. Maybe there are good reasons
for this, but it's my guess that there is still some part of ACT-R 2.0
in here, where q,r,a and b themselves were calculated from effort, r*,
b* and whatever. 
So a possible, attractive solution with respect to parsimony, is to use
P instead of q and r. (And, although it currently poses no direct
problems, use C instead of a and b). 

For the second problem I have no clear intuitions. Todds solution sounds
appealing, although I am not sure what he means by "Recognizing the
goal". It sounds like it can be done with the current architecture,
which is of course always more attractive than requiring changes.

Niels.
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Niels Taatgen
Technische Cognitiewetenschap / Cognitive Science and Engineering
Grote Kruisstraat 2/1
9712 TS Groningen, The Netherlands
Email: n.a.taatgen at bcn.rug.nl
WWW: http://tcw2.ppsw.rug.nl/~niels
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