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Diffstat (limited to 'paper2')
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| -rw-r--r-- | paper2/thesis.tex | 35 |
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diff --git a/paper2/thesis.pdf b/paper2/thesis.pdf Binary files differindex 9a6e3cc..ea0d55c 100644 --- a/paper2/thesis.pdf +++ b/paper2/thesis.pdf 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} |