problem solving in control of discrete-event systems lenko grigorov and karen rudie queen’s...
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Problem solving in control ofdiscrete-event systems
Lenko Grigorov and Karen Rudie
Queen’s University
Kingston, Canada
July, 2007 Grigorov & Rudie, Queen's Univ.
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Content
Motivation Observational study Data analysis methodology Results and discussion Future directions
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The look of DES software
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Problems with DES software No facilities to represent huge
models meaningfully (106+ states) Does not support much besides
performing DES algorithms Formalizing an informal model Verifying the output of algorithms Implementing supervisors
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How to address the problems? The problems with DES software are
complex No straight-forward solution
Study done by Rogers et al. on diagnosis from X-rays Understand cognitive processes Use information to design software
interface
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Goal
Understand human problem-solving strategy in control of DES
Create a model of the cognitive process
Use the model to guide the development of DES software
Test the new software to validate improvements
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Observational study
5 experts asked to solve DES problems Definition of problem: informal description Expected solution: formal model and DES
supervisor(s) Use pen and paper and/or software
Switch as many times as desired Verbalize thinking
Performance recorded with video camera
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Data encoding
Encoding activities along 4 main axes Type of activity
Perform with pen and paper, perform with computer, verbalize...
DES entity referred to Module, event, state...
Stage Inspection, verification...
Action Create, modify appearance, count...
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Data analysis – application
One video session encoded and analysed
Two periods Pen and paper Computer
Duration of activities N-gram analysis and clustering
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Data analysis – n-grams
N-gram analysis: the ratio of occurrence of a specific sub-sequence of n items in a larger sequence Sequence 'abcdbbc', 2-gram 'bc' Absolute ratio is 2/6 Relative ratio is 2/3
Relative to all n-grams which start with the same (n-1) symbols
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Data analysis – clustering
Unsupervised clustering: assign data items to separate classes
No prior idea of How many classes What the criterion of distinction is
Distance between items is bigger if Type of item is different Time between items is larger
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Analysis of references
Reference to entities DES modules FSA elements: states, transitions, events Computational algorithms
Reference to DES modules Machines 1 and 2 Buffers 1 and 2 Testing unit
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Output of N-gram analysis
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Output of clustering
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Preliminary results (1)
12 min pen and paper 7 min reading and understanding problem Rest for modeling
34 min computer 8 min (23%) improving layout of graphs Rest for input of models, DES algorithms,
verification and remodeling
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Preliminary results (2)
Subject works with “chunks” of related activities Type of entity: if working on states, not
likely to interrupt with work on events Module: if working on machine1, not
likely to interrupt with work on machine2
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Preliminary results (3)
Subject does not consider DES algorithms if thinking at the low level of states, transitions, etc. Only when thinking at the level of modules
Software seems to shape workflow Pen and paper: no predominant pattern Computer: modeling in the sequence
“module, events, states, transitions” This is the sequence supported by the software
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Discussion
Discrepancies between the two periods Different stages of problem solving Software imposes constraints
Graphical representation of model is very important
Software not suitable for conceptual modeling Subject chose pen & paper in the beginning
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Current research -conceptual modeling
In the initial stages of design, subjects Consider participants/
sub-systems and Interactions between
them
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Current research - frameworkfor conceptual modeling
Template design of DESs Inspired by observations Library with templates
of common behaviors Instantiate templates Link them No need to consider low-
level details
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Future work
Analyse all video sessions Improve encoding scheme Use other analysis techniques
Build model of problem-solving strategy What steps are taken What information is needed and when
Use model to improve DES software
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Queen’s University
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