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ongterm 
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This type of data compression into valid 
models is symbolized in figure 7 by the 
formation of a reduced tail on the past time 
axis (left). Quasi-static knowledge resulting 
from this is used lateron for triggering 
proper control activities depending on the 
situation encountered. Standard perturba- 
  
  
  
  
  
history of 
tie objects 
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abstracted experience v = 
————— treet) >, TO 7 
e modal elements for the in- S, s 
terpretation of sensor data 
e goal functions 
e control mode actuation 
e instantiation control extended 
long term memory presence 
  
  
  
' here and now’ 
Fig.7: Representation density over time 
Once this is represented, predictions of the state evolu- 
tion over time may be obtained at relatively low cost. Since 
usually neither the control nor the perturbation inputs of 
the future are known, prediction usually stops at one cycle 
(for the normal prediction-error-feedback state estimation 
process) or after only a few cycles in order not to incur too 
much uncertainty. For well known feedforward control 
time history inputs in order to achieve some maneuver 
element (for example lane change in road vehicle guidance 
with a sine-like steering angle input over time using proper 
parameters for period T and amplitude A) reliable predic- 
tions over longer temporal ranges (seconds) are possible. 
Taking standard perturbation statistics into account, even 
longer ranges over entire maneuver sequences may be 
meaningful (like prediction of the time needed to go from 
point A to B). In the average, however, the number of 
predicted events will vanish on the future time scale to the 
right. 
If good internal models are available for generating rich 
actual internal representations from the actual data 
measured, it will be impossible to store all these data as a 
'personal history of adventures'; it is not necessary, 
though. Since the time histories of the state variables may 
be regenerated from stored initial conditions and control 
as well as perturbation time history inputs once a proper 
model for the dynamic behavior is available, only the latter 
ones need be stored. For these again, instead of pointwise 
storing each individual time history, parameterized 
generic models would allow very efficient storage since a 
dense data input vector may be replaced by a few parame- 
ters needed to feed the proper function call. This shows 
that proper temporal models may be very efficient in 
reducing memory requirements if things are properly or- 
ganized. Past process state time histories and events may 
then be reconstructed actively from combining only a few 
stored historical data with stored model knowledge. This 
principle is the basic advantage of the 4D approach com- 
bining space and time in an integrated manner. 
ee 
— tions are counter-acted by feedback control 
laws which are implemented by a direct 
loop from the sensory data to the corre- 
sponding actuators (see center of fig.7) via 
internal state variables of recognized ob- 
jects; this allows stable behavior under per- 
turbed conditions without the explicit 
knowledge levels having to interact withthe 
high frequency data stream. Only unforseen 
situations and unpredicted new features dis- 
covered lead to an activation of the more 
knowledge based hypothesis generation 
partcontrolling the active set of internal dynamical models 
(lower left in fig.7). 
Seen from this point of view, the entire mental’ internal 
world of representations has as its purpose to provide the 
system with capabilities of data interpretation well suited 
for control outputs which enable the system to achieve its 
goals; previous experience may be exploited for this pur- 
pose contributing to the rating of a system as being intel- 
ligent or not. 
The 4D-approach 
For areliable description of mechanical processes in our 
everyday environment science has found the framework 
of threedimensional space and time to be well suited. 
Objects are defined in this environment as units having 
special properties or functions. For simplicity, we confine 
ourselves at present to rigid objects which may be moved 
as units having constant shape over time (e.g. vehicles, 
obstacles) or which are static parts of the environment 
(roads,buildings,installations etc.). Each object has a spa- 
tial shape, a position and an angular orientation in a 
framework relative to the observer, all in 3-D. Objects are 
classified according to their mobility: 1. Environmental 
objects are fixed to an environment and determine its 
visual appearance, like roads, road shoulders, trees and 
buildings, walls; 2. static objects are presently at rest, 
however, they may be moved or may even belong to the 
last class; 3. objects able of autonomous locomotion. The 
vehicle itself is an object of class 3, for which a model of 
its locomotion capabilities and of some basic geometrical 
properties are known. This includes the cause-and-effect 
relationships with respectto activating the controls and the 
state transition over time. In addition, the position and 
orientation of the vision sensor relative to those parts of 
the body interacting with the environment, i.e. the wheel 
base, are assumed to be known.