Welcome! This is the home page for The Computational Beauty of Nature, affectionately known as ``The Fish and Chips Book.'' Here, you will find information about the book, source code for simulations involving fractals, chaos, complex systems, and adaptation, and a whole slew of goodies for people interested in multidisciplinary topics involving computers, philosophy, and science.

This portal is based on and replaces the NLG Resources Wiki run by ACL SIGGEN from November 2005 to February 2009.

(still) in alphabetical order. It will soon be ordered on activity.

A more recent 2009 study, again at Lausanne, suggests that swarms of bots don't just evolve cooperative strategies to find food (or avoid poison), they can also evolve the ability to deceive. Bots equipped with artificial neural networks and programmed to find food eventually learn to conceal their visual signals from other robots to keep the food for themselves.

As the robot slides down the snowy mountain we can see an impressive ability to self balance, change direction, and autonomously sense and navigate around race gates.

A gyroscope, four sensors mounted between the skis, and motor position sensors support the lower control of stability and joints.

A simple USB camera and a GPS system allow the robot to gather the necessary input to determine its position and calculate what action it should take next.

Are we humans – with our carbon-based neural net “wetware” brains – at a point in history when we might be able to imprint the circuitry of the human brain using transistors on a silicon chip?

A well-covered recent article in MIT's Technology Review reports that a team of European scientists may have taken the first steps in creating a silicon chip designed to function like a human brain.

This competition is about learning, or otherwise developing, the best controller (agent) for a version of Super Mario Bros.

The controller's job is to win as many levels (of increasing difficulty) as possible. Each time step (24 per second in simualated time) the controller has to decide what action to take (left, right, jump etc) in response to the environment around Mario.

Current handcrafted approaches to compiler development are no longer sustainable. With each generation of re-configurable architecture, the compiler development time increases and the performance improvement achieved decreases. As high performance embedded systems move from application specific ASICs to programmable heterogeneous processors, this problem is becoming critical.

This project explores an emerging alternative approach where we use machine-learning techniques, developed in the artificial intelligence arena, to learn how to generate compilers automatically.

These are short samples of the music generated by the program Randomusic, written by Magnus Andersson.