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Engineering
andMusic
Version:
1.3
31.
August 2001 |
|
Real time Dynamic Decision Making
in Supervisory Control
Dr.-Ing. Leon Urbas
Abstract
The paper outlines some attributes of dynamic human-machine systems which
are relevant to classifiy them as real time dynamic decision making systems
from the persepctive of the human supervisory operator.
Characteristics of Dynamic Human Machine Systems
Our research has its focus on modelling of cognitive behaviour of human
operators in dynamic human-machine-systems. The class of systems we are
looking at can be characterised by the following attributes:
-
Dynamic system: the technical system shows a characteristic dynamic behaviour,
which is not fully changeable due to some limitations of the system. The
future development of the technical system without outer influence is determined
by some memory in form of energy, mass or information storage.
-
coupled multiple inputs multiple outputs: Single manipulated variables
(input) of the system show influence on more than one observable state
variable of the system. This can be the cause for conflicting goals. For
instance, raising the throughput in processes of the chemical industries
often shows a contra productive effect in product quality.
-
latent variables: The knowledge of manifest (direct observable) variables
is not sufficient to interpret current or anticipate future behaviour of
the system. Instead it is necessary to deduce latent variables from observation
of manifest and manipulated variables.
-
time variant dynamics: endogenous disturbances or deliberate changes in
topology of the system may have great influence on the dynamic behaviour
of the system due to new or fading interactions between different parts.
-
open: the technical system is affected by exogenous disturbances which
most often are not direct observable - it is necessary to deduce them from
unusual behaviour of the system.
-
real time: activities or sequels of activities have to be executed until
certain deadlines to reach the intended goal. There are no means to stop,
freeze or rewind the technical system.
The characteristics mentioned above, especially the ad hoc unknown
latent variables and the exogenous disturbances make the decision-making
problem ill-defined: start and end of the problem are unknown and may change
during the problem solving process. Due to the real time characteristics
the time available is limited. Latent state variables and internal coupling
of variables complicates the acquisition of accurate knowledge about the
system. In consequence only limited time and uncertain knowledge is available
to the responsible operator to judge about new situations, make a decision,
and put goal oriented activities into execution. The mental models in such
task environments, that can be deduced from learning through interaction
and observation are generally only partial homomorphous, i.e. we assume
that the relevant structure of the system can be mapped only partially
on the mental model. This makes sense from an economic perspective: the
requirements for a functional mental model which is useful for the control
of a single variable are fundamentally different from the requirements
for a structural qualitative mental model which helps in failure diagnosis.
Decision Making under Pressure of Time
Rational behaviour in real time dynamic decision-making systems in the
sense of good adaptation to the task environment makes it necessary to
revert to strategies, which reduce the need for time and cognitive processing
resources. We assume that generally strategies with low execution time
and low demand for cognitive resources are chosen, as long as the subjective
necessary power of anticipation can be reached. How effort and power of
anticipation may be
represented or calculated in a cognitive architecture is not clear
at the moment and object of research. To clarify things, some examples
for generic strategies are sketched which differ in their need of time
and the demand for cognitive resources:
-
a priori strategy: The current state is anticipated by the mental model
and recent observations, and may be followed by an activity where some
critical variables are compared.
-
a posterior strategy: The current state is reconstructed from observation
of current data over some period and may be some historical data. This
passive strategy may be coupled with well-directed manipulation of some
variables.
-
erratic strategy: interact by random and hope for the best.
It depends on the task environment, whether a strategy is successful and
in this sense adequate. The strategy of choice may be influenced by the
structure of the domain, the task itself, the necessary level of detail
and the engineering design of the supervisory control systems, i.e. the
task sharing between automation and human operator as well as the design
of the interface.
Operator Modeling and Music
If we want to compare the conductor and his orchestra with the supervisory
operator and his technical system from the perspective of common cognitive
models, the author believes, that it is necessary to have a close look
on the
characteristics of the dynamic task environment, the tasks, the problem
solving strategies and the actions which can be compared or have to be
distinguished. The workshop is a highly welcome opportunity to start with
this task. Some questions, which arise from current considerations, are:
Is every orchestra able to play any score? If not, what are the limits?
Is conducting a real time decision making task? What are musicians doing,
when the score assigns their instrument to pause? |
