Problem solving in control of discrete-event systems Lenko Grigorov and Karen Rudie Queen’s University Kingston, Canada

Problem solving in control ofdiscrete-event systems

Lenko Grigorov and Karen Rudie

Queen’s University

Kingston, Canada

July, 2007 Grigorov & Rudie, Queen's Univ.

2

Content

Motivation Observational study Data analysis methodology Results and discussion Future directions


3

The look of DES software


4

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


5

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


6

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


7

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


9

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...


10

Data analysis – application

One video session encoded and analysed

Two periods Pen and paper Computer

Duration of activities N-gram analysis and clustering


11

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


12

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


13

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


14

Output of N-gram analysis


15

Output of clustering


16

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


17


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


18


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


19

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


20

Current research -conceptual modeling

In the initial stages of design, subjects Consider participants/

sub-systems and Interactions between

them


21

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


22

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


23

Queen’s University

Documents

Problem solving in control of discrete-event systems Lenko Grigorov and Karen Rudie Queen’s University Kingston, Canada