humans in loops – neocybernetic view at complex processes presentation at automation days 2009...
TRANSCRIPT
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Humans in Loops –
Neocybernetic View at Complex Processes
Presentation at Automation Days 2009
Heikki HyötyniemiHelsinki University of Technology
Automation and Systems Engineering
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Process levels – EXTENDED
Physical level (formation): Input Qin, output Qout; based on the actual process structure and its energy/matter flows
Information level (metaformation): Inputs Qin and Qout, output h; based on what can be measured and what can be affected
Knowledge level (metainformation): Inputs are now the tensions of the system, outputs are its resulting properties
FC
Qin
Qout
LI
h
FCQin
Qout
h
“How to enhance the measurement?”
Emphasis now
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From process flows to information flows ...
Controls
Controllerdynamics
-+
Actuatordynamics
Measurementdynamics
Processdynamics
(zoom)
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... From information flows to “knowhowflows”
Large amounts of appropriately chosen external information and understanding
Controllerdynamics
-+
Actuatordynamics
Measurementdynamics
Processdynamics
Referen
ce
Error
Contro
l
Inpu
tOutp
ut
Feedba
ck
Distur
bance
“Controls”
ux+ B
A
(zoom) “Eksoinformation”
“Endoinformation” (found within the system)
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Role of neocybernetics
Evolution that is based on local agents: special structure remaining visible in the closed loop on the global level
Interpretation: degrees of freedom in the directions of interest, balance among tensions (physical + economical)
System becomes better and better balanced and robust against environmental disturbances
This applies also to “artificial evolution” implemented by humans when developing industrial plants
Neocybernetics offers tools for implementing “higher-level control” in the spirit of “second-order cybernetics”
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Now, there are humans in the system structure...
First, study a simple case: Linearity assumed Process linearized around a single operating point Mental model reduced to a set of quality measures
Then, degrees of freedom reduce to principal component subspace axes
Higher-level view: abstract the signals away, concentrate on qualifiers vs. qualities
Utilize the low-dimensional mapping between the qualifiers and qualities in parameter optimization, etc. ...
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Experiments with the IRT method
Veitsiluoto case:Pulp digester
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6Quality measure 1
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Optimization in reduced-dimension parameter space
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Extending the operator view
PID
A R S
PID
PID
PID
PID
PID
Explicit optimization is often not possible
The slopes of criteria can still be found
The tuning knobs can be made to reflect relevance
Lower-level controllers tuned in
a coordinated manner according to a model where
control parameters affect qualities
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“Neocybernetic expertise”
More general case: Process consisting of many submodels, expertise with various categories = prototypes
Observation/measurement data correlations are detected and modeled in terms of sparse coded features
Categories/concepts (subsymbolic) can be implemented using sparse coding in a cognitively plausible way
Reasoning/inference becomes a static pattern matching process in a high-dimensional space
Process/mental structures share the same sparse coded model structures and can thus be combined!
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“Conceptual froth types” at Pyhäsalmi
How operators see the froth
“Stiff”
“Wet”
“Dry”
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Sparse coding captures categories!
Scores of “hand-coded” features
Scores of sparsely coded features
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Delphi method
Exploitation of expertise “The Delphi method is a systematic, interactive forecasting method
which relies on a panel of independent experts. The experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Thus, participants are encouraged to revise their earlier answers in light of the replies of other members of the group. It is believed that during this process the range of the answers will decrease and the group will converge towards the "correct" answer. …”
Automation of the method (cf. Case-Based Reasoning): In industry, operator expertise is directly available (measurements +
actions) This information only has to be compressed using the cybernetic
models Claim: the neocybernetic model of tensions is just right for this purpose
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How about different kind of expertise?
H. Hyötyniemi: Expertise: an optimized data model? (STeP 2000)
Data courtesy of Neles
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Idea of “Second-Order Cybernetics” exploited
Symmetry: The complex systems “observe” each other, constructing neocybernetic pattern models of each other
Recognition ofprocess patterns
Recognition ofmental patterns
Process domain
Mental domain
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“Second-Order Control” in general
Neocybernetic intuitions combined
Process
StateControl
Processpatterns
Actionpatterns
Subspace
Featureextraction
Featureextraction
Human
Learn
ing
Learn
ing