Poslední úprava: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (04.02.2019)
Evolving Structures in Mathematics
Computing Machinery and Intelligence, A. Turing
- We will discuss some early ideas about artificial intelligence, and high-level overview of topics such as Turing-completeness.
Society of Mind, Marwin Minsky
- In this book, Marwin advocates that complex intelligent behavior is a result of cooperation of simple agents, and that the human mind can be explained this way.
The Quark and The Jaguar, Murray Gell-Mann
- Occam's razor, Minimum description length, Kolmogorov complexity, Algorithmic probability, measures of complexity proposed by Gell-Mann
L-systems: Mathematical Models for Cellular Interactions in Development, A. Lindenmayer; Wikipedia
- parallel string rewriting grammars that can generate objects that resemble those found in nature (leaves, trees); the grammars can be very trivial, while the objects may appear complex to us
Fractals: The fractal geometry of nature, B. Mandelbrot
- Fractals are objects that appear the same at different scales, while some appear rather complex to us.
Von Neumann's Self-Reproducing Automata, A. W. Burks
- Conway's Game of Life can be seen as a simple example how cellular automatons work. However, the ideas here are deeper than they appear at first, and we can see that the original motivation for the development of cellular automatons was to design mathematical structures that can copy themselves in a non-trivial way, and possibly increase in complexity while doing so.
Studying Artificial Life with Cellular Automata, C. G. Langton
- Deals with mathematical structures that can have similar properties to how we define life: self-reproduction, evolution.
Genetic Algorithms, J. Holland
- We will discuss the basic ideas behind evolutionary and genetic algorithms and genetic programming, and compare these algorithms with the previously discussed attempts to design objects that can evolve.
- Evolving neural networks through augmenting topologies, K. O. Stanley and R. Miikkulainen
- Another attempt to simulate evolution that uses neural networks. In this talk, we will briefly discuss the basics of artificial neural networks, and extend these to models that can grow in complexity.