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An Evolutionary Architecture - John Frazer

An Evolutionary Architecture - John Frazer
Electronic version of An Evolutionary Architecture An Evolutionary Architecture was published in January 1995 to coincide with an exhibition of the same name at the Architectural Association. It concentrates on the work of Diploma Unit 11 run by John and Julia Frazer (with Pete Silver and Guy Westbrook) between 1989 and 1996, but includes formative work by the author dating back to his diploma prize-winning project at the AA in 1969 and related research work at Cambridge University. The book investigates the fundamental form-generating processes in architecture, considering architecture as a form of artificial life, and proposing a genetic representation in a form of DNA-like code-script, which can then be subject to developmental and evolutionary processes in response to the user and the environment. The Interactivator could also reproduce itself by allowing the program of the model to be downloaded to remote sites. See Further Development www.autotectonica.org

John Conway's Game of Life The Game The Game of Life is not your typical computer game. It is a 'cellular automaton', and was invented by Cambridge mathematician John Conway. This game became widely known when it was mentioned in an article published by Scientific American in 1970. playgameoflife.com New developments of this page will continue on playgameoflife.com. playgameoflife.com The Simulation Figure from the XKCD RIP John Conway comic. The Rules For a space that is 'populated': Each cell with one or no neighbors dies, as if by solitude. Each cell with four or more neighbors dies, as if by overpopulation. Each cell with two or three neighbors survives. For a space that is 'empty' or 'unpopulated' Each cell with three neighbors becomes populated. The Controls Choose a figure from the pull-down menu or make one yourself by clicking on the cells with a mouse. Development Game of Life is supported by Dotcom-Monitor, LoadView Testing, Web Hosting Buddy, Instructify, Security Guard Training Central, and Driven Coffee

Wiener While it is impossible to assign credit for the conception of Cybernetics to a single individual, certainly one of the people most responsible for its development and popularity as well as its theoretical shape was the mathematician Norbert Wiener (see Wiener, Norbert). Wiener wrote several popular books in which he coined the word "cybernetics," elaborated on its central concepts, and sought to illuminate its ethical and social implications. In so doing, he helped to spread the insights to be gained from studying control and communication systems, and also to heighten an awareness of the ethical and social consequences of automation and mass communication. As the son of one of the world's leading linguists, Wiener's etymology of "cybernetics" is itself instructive of his philosophy. Wiener did not coin the word until the summer of 1947, but he began the work leading up to the theory behind it in 1941, and published his theory in 1943. by Peter M. 1153 words For Further Research

Table of Contents for AI: A Modern Approach Chapter 1: Introduction ... 1 1.1. What Is AI? Chapter 2: Intelligent Agents ... 34 2.1. Chapter 3: Solving Problems by Searching ... 64 3.1. Chapter 4: Beyond Classical Search ... 120 4.1. Chapter 5: Adversarial Search ... 161 5.1. Chapter 6: Constraint Satisfaction Problems ... 202 6.1. Chapter 7: Logical Agents ... 234 7.1. Chapter 8: First-Order Logic ... 285 8.1. Chapter 9: Inference in First-Order Logic ... 322 9.1. Chapter 10: Classical Planning ... 366 10.1. Chapter 11: Planning and Acting in the Real World ... 401 11.1. Chapter 12: Knowledge Representation ... 437 12.1. Chapter 13: Quantifying Uncertainty ... 480 13.1. Chapter 14: Probabilistic Reasoning ... 510 14.1. Chapter 15: Probabilistic Reasoning over Time ... 566 15.1. Chapter 16: Making Simple Decisions ... 610 16.1. Chapter 17: Making Complex Decisions ... 645 17.1. Chapter 18: Learning from Examples ... 693 18.1. Chapter 19: Knowledge in Learning ... 768 19.1. Chapter 20: Learning Probabilistic Models ... 802 20.1. 21.1. 22.1.

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