update

1 files changed, 33 insertions, 2 deletions

diff --git a/paper2/thesis.tex b/paper2/thesis.tex
index 0055337..23e19fd 100644
--- a/paper2/thesis.tex
+++ b/paper2/thesis.tex
@@ -63,7 +63,7 @@ Cognitive Architectures, modeling learning, production systems, ACT-R
 
 \subsection*{Productions}
 
-\todo[inline]{Productions are Rules with a condition and an action. Example production. can interact with various modules (memory, vision, motor)}
+\todo[inline]{Productions are Rules with a condition and an action. Example production. can interact with various modules (memory, vision, motor), Overview of production systems?}
 
 \begin{table}[hb]
 \caption{Example Production}
@@ -86,10 +86,39 @@ Cognitive Architectures, modeling learning, production systems, ACT-R
 
 \todo[inline]{Retrieval(activation) strength, utility learning, production compilation, ...}
 
+There are a variety of methods production systems use to model learning.
+When multiple productions are applicable to the current state, the production that the model thinks is the most useful should be selected.
+How useful a production is can be learned while the model is running and is modeled in ACT-R through a reinforcement learning like process called utility learning.
+
+Oftentimes a series of productions need to be executed in order, this can be combined in to a single production which does all of the actions at once, saving time deciding on which production to use.
+When two productions are successfully called in a row, a production compilation process is started and combines both into a single production if possible.
+Since the compiled productions are specific to the buffer values when the compilation was done, there can be many different combined productions of the same two productions.
+E.g. a production starting retrieval of an addition fact and a production using the retrieved fact can combine into specific addition-result combinations, skipping retrieval.
+Depending on the addends, this compilation then produces different combinations like add1=1 add2=1 then sum=2 do stuff \todo{figure of solo productions and of compiled productions} \
+
+allegory? learning general production from specific ones (not used)
+
+ACT-Rs subsymbolic system also models delays and accuracy of the declarative memory, where retrieving memories can fail based on their activation strength.
+Activation strength increases the more often a memory is created or retrieved.
+
+
+
 \subsection*{Task}
 
 \todo[inline]{Modified Frensch/Elio Task. 7 mathematical procedures, learning differently based on presentation order}
 
+Task desc
+
+How modeled:
+Improvements in task performance are mainly dependent on production compilation, as the order and how efficiently the mathematical operations are performed are the main subject of the task.
+Utility learning matters mostly on production selection and ordering, however the task itself is mostly linear.
+It can still play a significant role if alternative or shortcut productions for mathematical operations exist.
+E.g. a production that swaps argument 1 and argument 2 in addition or multiplication may reduce time spend, dependent on how the algorithm functions.
+
+The subsymbolic system of ACT-R also involves mechanisms to gauge retrieval chance and activation strength in the declarative memory.
+This is used to model learning and retrieval of new memory chunks.
+In this task however, the subject already has knowledge of mathematical facts and \todo{``not learn new facts really during exp''} \
+
 \begin{table}[hb]
 \caption{Experiment Procedures.}
 \label{tab:proc}
@@ -257,7 +286,9 @@ Since operations use both the full numbers and their digits, a set of production
 
 \subsection*{Working with ACT-R/pyactr}
 
-\todo[inline]{no basic productions given, everything has to be implemented from scratch, papers using act-r very rarely publish their model code}
+\todo[inline]{no basic productions given (aside basic tutorial code), everything has to be implemented from scratch, papers using act-r very rarely publish their model code}
+\todo[inline]{}
+\todo[inline]{no/confusing task switching/subgoals}
 \todo[inline]{this model uses many different operations and modules of ACT-R and has to model each from scratch and handle task switching}
 \todo[inline]{vis: relative positions are not implemented, the visual search loops had to be unrolled to the required number of iterations and is not general}