The "Game of life" model was created in 1970 by the mathematician Jonh Horton Conway
using cellular automata. Since then, di erent extensions of these cellular automata have been
used in many applications, such as car traffic control or baggage traffic in an airport.
These extensions introduce ideas not only from cellular automata models but also from neural
networks theory.
In this work, we introduce probabilistic cellular automata which include non-deterministic
rules for transitions between successive generations of the automaton together with probabilistic
decisions about life and death of the cells in next generation of the automaton. This way,
more realistic situations can be modeled and the obtained results are also non-deterministic.
As an example of use, an implementation of this probabilistic cellular automaton has been
developed using it for simulating tissues evolution. The authors are specially interested in
simulations of cancerous tissues.
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