GAP-JOURNAL 2012/13 - AFA

72
Creating the ESC’s algorithms, weattach to it aspecific perception ofour world –the
model (a representation ofthe car and its close surrounding) –and the possibility to
decide within the model whether things are true or false (false being adangerous situation
here).
While we could also discuss the way the system finds an appropriate reaction toadangerous
situation, Iwould like toconcentrate here on the even more basic question what
is wrong or false within agiven model. We can think of our model asthe totality of
input (sensor data) atacertain time or even all the perceived data since its creation
(having memory size asaconstraint). Then wecreate apredicate (a function, that only
returns “yes” or“no”) with our entire model –or part of it –asinput, that should yield
us all the true and false things describable inour system (secure and dangerous situations).
Making those abstractions, we see that complex systems ofthe second approach require
some kind of automatic logical inference. We can either have alist ofthe entire true or
the entire false states in our system, orwecan describe away of deducing (or: proving)
acertain state to be true.
An apparently simple system could beasfollows: Randomized input generates us over
time every possible statement that ismeaningful (more exact: is well-defined) within
natural number arithmetic and first-order logics. That could be“1+1=0” (of course
false)or“0+1+0