A Computational Framework for Intelligent Systems

Because we want to use engineered intelligent systems in the realm of computer science we have to map the engineering framework further into a computational framework. The basic concept of all computation is the automaton, which means in the full form a turing machine^2.4. Every other kind of formalism in the realm of computer science is either a subconcept of a turing machine or it does not belong to computer science.

Figure 2.7: Computational Framework For Intelligent Systems

Mapping the evolutionary and the engineering framework into a computational framework is rather straightforward (cf. figure 2.7).

1. Automaton: The starting point is the concept of an automaton $A_{i}$ which consists of a finite table of commands telling how the read-write head has to be moved on the tape and how the automaton reacts to the input.
2. Translation: To use the idea of biological systems based on genetic information $GI$ one has to establish a translation algorithm $\tau: GI \longmapsto A$ to map a genetic information into a working automaton.
3. Population: A population $POP$ then is given by a set of genetic informations $GP = \{ GI_{1}, \cdots, GI_{n}\}$ called gene pool as well as a set of automata $POP=\{A_{1}, \cdots, A_{n}\}$ generated by a translation from the genetic informations given in the gene pool.
4. Mating: During their life time will the members of the population occasionally directly exchange their genetic information through a mating operation $mating: GI \times GI \longmapsto GI$. The new genetic information is added to the gene pool. When a member terminates its process its genetic information will be deleted in the gene pool.
5. World Interface: The original tape of the automaton is expanded to a multi tape representing an interface between the population and the assumed world $W$.
6. World: The world is a function $f_{W}$ 'behind' the interface which can read and write onto the interface. The world is either some part of the real world which is interfacing with the population or a virtual model which mimics some properties of the real world. In ideal cases the world can be stationary, but the usual case is a non-stationary world which is a mixture of partial regularities and noise based on many random-like processes.