[ACT-R-users] Question related to the Activation of Chunks and Base-Level learning

[Arun Krishna]

Hi,

I have a question related to the Activation of Chunks and Base-Level
learning.

*Problem Description:*

In the model I am using, the base level learning value is not getting
changed when I use the chunk merging. I am using add-dm command with python
API, where I add the chunks with same slot values and was expecting the
base level learning for the chunks getting increased.

*Reference:*

ACT-R\tutorial\unit4\unit4.pdf,  4.3.2 Chunk Merging section.

*Code:*

In the model I am using I had set the sub symbolic computation

 (sgp : esc t) and (sgp :rt -0.5).

The complete setting for the model is given below

“(sgp :v t :esc t :rt -0.5 :lf 0.4 :ans 0.5 :bll 0.5 :act nil :ncnar nil
:ul t)

(sgp :seed (200 4))”



I am using the python API for add-dm

actr.add_dm(

            [chunkName, "isa", "navigation", "navigationStart",
currentState, "navigationEnd", "state-16",

             "navigationTrigger", trigger])

This API is getting triggered with different chunk names. Chunk-1,
chunk-2,…..chunk 50. All the chunk has same slot values and my expectation
is that the base level learning of every chunk will be modified.

But when I use (pprint-chunks-plus chunk-3) I get

  SIMILARITIES  NIL

   REFERENCE-COUNT  0

   REFERENCE-LIST  NIL

   SOURCE-SPREAD  0

   LAST-BASE-LEVEL  0

   BASE-LEVEL  NIL

   CREATION-TIME  0

   FAN-IN  NIL

   C-FAN-OUT  0

   FAN-OUT  0

   IN-DM  NIL

   ACTIVATION  0

   BUFFER-SET-INVALID  NIL

Could you please let me know how to modify the base level learning and
reference count of these chunks (Which supposed to point to a single chunk
with different reference names since the slot values are same) ?

Thanks & Regards,

Arun
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