Wissen gewinnen und gewinnen durch Wissen

Fent, Thomas (2000): Wissen gewinnen und gewinnen durch Wissen. Unpublished.

Abstract

According to Alfred Korzybski (1921) humans unlike plants and animals have the property to bind time, i.e. they are able to transfer experience through time. Humans are capable to collenct knowledge from the past and communicate their knowledge to the future. This paper investigates the possibilities and limitations to describe this capability by algorithms and implement it in artificial learning systems.

• Item Type: MPRA Paper
• Institution: Vienna University of Economics and Business Administration
• Language: German
• Keywords: knowledge; game theory; artificial adaptive agents; learning classifier systems; genetic algorithms
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques; Simulation Modeling ; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C61 - Optimization Techniques; Programming Models; Dynamic Analysis; C - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C72 - Noncooperative Games; C - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C73 - Stochastic and Dynamic Games; Evolutionary Games; Repeated Games

References

Banzhaf, W.; Nordin, P.; Keller, R. E.; Francone, F. D. (1998), Genetic Programming, An Introduction, On the Automatic Evolution of Computer Programs and Ist Applications. Morgan Kaufmann Publishers.

Binmore, K. (1992), Fun and Games, A Text on Game Theory. D.C. Heath and Company.

Friedberg, R. (1958), A learning machine, part I. IBM J. Research and Development.

Godlberg, D.E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company.

Korzybski, A.H.S. (1921), Manhood of Humanity: The Science and Art of Human Engineering.

Osborne, M.J. (1994), A Course in Game Theory. The MIT Press.