Preface

This on-line booklet is a continuation of my script General (Behavior Based) Computational Learning Theory (see: http://www.uffmm.org/gbbclt.html). Collecting additional new ideas I hope to make it a bit more concise than the old one. But, as everybody in the field knows, the main inhibiting factor to do this is 'time'. The workload with teaching and administrative work is always very high hindering a more extensive writing. But one should state it clearly: our students are a major source of motivation, to do this work; they are inspiring and they very often introduce exciting ideas to clarify questions. And, these students are the 'generation of the upcoming future', which has to be mastered to keep life 'alive' in the universe.

This booklet has two other accompanying texts: at http://www.uffmm.org/EIESS-SIM/eiess-sim/ you will find a tutorial for the scilab programs and exercises. Additionally the students - especially Micharel Bittorf and andre Freudenreich - have set up a new public server for experiments with intelligent agents. We are planning to announce this server from as official platform in 2014. Until that time there is only an inofficial usage.

I like to mention two friends, which are important for this new revision: One is Ricardo Gudwin (Unicamp, Brazil) and the other Alexander Mehler (Frankfurt am Main, Germany). Knowing each other for many years we have intensified our collaborations gradually since the last year. While Alexander and I collaborate in a very direct level between our both universities in discussions, on a project level and through our students, Ricardo is connected to us by regular talks through the internet. Ricardo has also decided to cooperate directly with this booklet to improve this new revision.

For this revision the original title 'General Behavior Based Computational Learning Theory (GBBCLT)' has changed several times. The 3rd version was 'Re-Engineering of Consciousness and Intelligence (RECaI)'. The 4th version has been changed to 'Reverse Engineering of the Mind (REtM)'. The actual 5th version is renamed again as you can read. This repeated variations of the title indicate that the subject matter allows different perspectives with different arguments. On the meta-level of theory building one has usually more than one option. The main arguments for the 5th version are the following ones:

1. Semiotic Systems: The main goal of this lecture is the engineering of technical systems which can behave like 'semiotic' systems ('semiotic' mainly in the tradition of de Saussure [320], Morris [263], and Peirce [286]. For an overview see Noeth [274]): the systems shall be able to learn and to use 'signs' in different ways for to communicate with human persons 'sufficiently well' .
2. Intelligence: We require that these semiotic systems are 'intelligent' in the sense that they can 'learn' and that they can act with a 'sufficient autonomy'. We will use the paradigms of experimental psychology and their 'IQ-concept' for the construction of benchmarks (cf. Doeben-Henisch [72], [75], [76]).
3. Evolutionary: Generally should all semiotic systems be embedded in a formal framework, which allows the realization of an 'evolutionary process' in the reproduction of these systems (cf.for first ideas Davies [56], Goldberg [122]).
4. Engineering: A technical solution has to be realized through an engineering process which is guided by general principles of systems engineering (cf. [73], [76], [96], [97]). A special topic is the concept of computation as introduced by Goedel [118] as well as Turing [384]. There are two known extreme positions for the usage of the concept of computation. One position assumes that the Goedel-Turing concept is to 'narrow' to be able to represent the full spectrum of the properties associated with 'intelligent semiotic' systems. The other position assumes that the mathematical concept of the universal turing machine is 'powerful enough' to represent all 'essential' properties sufficiently well (at least Turing himself had this position (cf. [385])). In this booklet is - by methodological reasons - the second position assumed. The rationality behind this is that the probability to show the limits of the second thesis is higher than to show the necessity of the first position. One of the weak points of the first position is the fact, that it is still not clear in which sense the turing machine concept fails to model 'intelligence'
5. Not Reverse: In the 4th version we have also used the term 'reverse' in connection with 'engineering'. But Gerd has dropped this term because it is methodologically not possible to define sharply what it means to use empirical knowledge about biological systems without saying that the covering of the biological 'originals' is complete. That there is a strong attitude to use knowledge about biological systems can be seen in the following texts, but we as engineers will not construct 1-to-1 mappings. This is the job of the empirical sciences.

6. No Mind: In the 4th version we used as an important term also 'mind'. This term has been dropped by Gerd too because there is no operational definition around in any discipline which can be used as point of reference. Clearly we have everyday 'intuitions' and 'heuristics' as 'mindful creatures' which can guide us in the understanding of phenomena, but there is no chance to use this concept within an empirical science or an engineering process without becoming 'arbitrary'. Instead of 'mind' or 'mindful' we will use the operationally defined term 'intelligence' ^1.1.

(Some month later things have changed again. But I will not have time to write this down before March 2013.)

The final goal of our research is to be able to construct some artificial semiotic systems to be able to communicate with human persons sufficiently well that they can assist and support men especially there where our limits endanger life and hinder quality^1.2.

Whatever we will do here, this can only be a small fragment in the universe of knowledge of today sciences. But this is the fate of all humans: our brain is small and the 'big picture' has to be constructed by many small pieces fitting together.

Let us start to get the job done.