Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

16.6 Application in Autopilots
Fig. 16.2 describes a cognitive map for an automated pilotless car driving
system. A vision system attached to the car receives visual information about
the road traffic and the pedestrians crossing the road. The belief of the
received signals is then mapped to the appropriate nodes (places) in the
cognitive map. The weights trained through unsupervised learning cycles are
also mapped to the appropriate arcs before the reasoning process is initiated.
The reasoning process continues updating the belief strength of the nodes
based on the signal strength of the received information and the weights. The
action is taken based on the concluding node with the highest belief.
Let the initial weights be as follows.
w41(0) = 0.95, w43(0) =0.85, w46(0) = 0.75, w84(0) =0.8, w85 (0) = 0.4, w11,7(0)
= 0.9, w12, 8(0) = 0.85.
Let us also assume that the thresholds associated with all transitions = 0.1
and the mortality rate α = 1.8. The P and Q matrices are now constructed and
the equations 16.15, 16.14 and 16.18 are recursively executed in order until the
steady-state in weights occurs. The steady-state values of weights, presented
below, are saved for use in the recognition phase.
Steady-state weights: w41* = 0.25, w43* =0.25, w46* = 0.25, w56* =0.17,
w84* =0.31, w85* = 0.33, w11,7* = 0.2, w12, 8* = 0.2 and all other wij =0.
In recall phase suppose we submit the belief vector N(0) and the cognitive
network generates an N*, where
N(0) = [0.2 0.3 0.4 0.0 0.0 0.3 0.35 0.0 0.4 0.3 0.0 0.0] T and
N* = [0.2 0.3 0.4 0.25 0.17 0.3 0.35 0.33 0.4 0.3 0.17 0.17] T .
It is observed from N* that among the concluding places {p4, p5,p8 ,p11,
p12} p8 = ‘rear car speed decreases’ has the highest steady-state belief. So,
this has to be executed.
16.7 Generation of Control Commands
by a Cognitive Map
With gradual learning and self-adaptation [5], the cognitive memory in
human brain builds up a control model for muscle movements and determines
the valuation space of the control signals for the execution of a task. For