Ms. Guidance on Artificial Life

see also: Ms. Guidance on Robotics




List of Newsgroups, Journals, Mailing Lists


    Genetic Programming at NASA.

      1. Genetic Programming
        Software * Bibliographies * Papers * GP FAQ * GP Tutorial *

      2. Genetic Algorithms
        Software * People * Papers * Bibliographies * Tutorials *

      3. Artificial Intelligence

        FAQ * Newsgroups * Machine Learning * Fuzzy Logic * Neural Nets * Robots *

    MIT Artificial Intelligence Laboratory Home Page

    CMU Artificial Intelligence Repository

      The CMU Artificial Intelligence Repository was established by Mark Kantrowitz in 1993 to collect files, programs and publications of interest to Artificial Intelligence researchers. This repository is by any means useful, if you realy want to conduct a research on Genetic Algorithms use the available search tool.

    ANU Bioinformatics

    Illinois Genetic Algorithms Laboratory (IlliGAL)

      The work at IlliGAL includes theoretical developments and practical applications of GAs. For more information about IlliGAL investigations, see the IlliGAL ARCHIVE (orderform) which contains technical reports, journal articles, and source code.

    The Genetic Algorithms Group

      Under the direction of Kenneth A. De Jong, the Genetic Algorithms Group (GAG) at George Mason University in Fairfax, Virginia. Group members are working on a variety of research projects including GA theory, coevolutionary algorithms, decentralized GAs, representation issues, evolutionary microeconomics, the application of GAs to molecular biology, and GA-based machine learning.
      There is a list of publications. PostScript copies of most of these papers are available online.

    Genetic Algorithms Research and Applications Group (the GARAGe)

      The GARAGe at Michigan State University is a multi-disciplinary unit interested in the application of Genetic Algorithms and Genetic Programming to real-world problems, as well as fundamental research on GA and GP. They have a number of projects including: parallelization of GAs/GPs; multiple population topologies and interchange methodologies; scheduling applications.

    Genetic Programming Home Page by Adam Fraser

      Software Available:

      1. Genetic Programming in C++ (Version 0.40) Source Code...
      2. Genetic Programming in C++ (Version 0.40) Documentation...
      3. Koza's Genetic Programming in LISP

    Matthew's GAlib:

      This library provides the application programmer with a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program. It is maintained by Matthew Wall.

    Mark Smucker's Evolutionary Computation and Artificial Life page

      1. Publications, Digests, Movies, Displays, and Journals
      2. Machine Learning and Distributed AI
      3. Economics!
      4. People: An excellent list of people and groups involved in working with complex adaptive systems.

    Uncertainty in Artificial Intelligence

      The Association for Uncertainty in Artificial Intelligence (AUAI) is a non-profit organization with the main purpose of running the annual Conference on Uncertainty in Artificial Intelligence (UAI). The UAI conference has been held every year since 1985, adjacent in space-time to NCAI (the AAAI conference) or IJCAI.

    Investigations of Design using GAs for Composite Material Structures(2D) and 3D beams

      Related Material:

      IEEE-Expert94, Design Using Genetic Algorithms: Some Results for Composite Structures.
      PDP94, Coarse-grain Genetic Algorithms, Categorization and New Approaches.

    "Genetic Programming for Articulated Figure Motion"

      By Larry Gritz and James K. Hahn., Journal of Visualization and Computer Animation, vol. 6: 129-142 (1995).

      Applying the AI technique known as Genetic Programming (GP) to control the motion of articulated figures. This allows the system to automatically generate life-like motion for jointed figures. The human animator must provide a fitness function which rates the motion which the system generates.

    Animation of the evolution of the GA population when optimizing a multimodal function
    by Juha Haataja

    Visual Models of Morphogenesis: A Guided Tour

      The material contained in this hypertext document is based on two papers by Przemyslaw Prusinkiewicz. The first appeared in the Proceedings of Graphics Interface '93 [Pru1993] and the second in Artificial Life [Pru1994a]. The topic of the papers is the development of biological structures.

      The document is separated into sections, each with its own HTML page. The pages themselves contain no inline images (except for navigational icons). Hopefully, this will make the paper easier to read with terminal-based browsers. However, contained in these pages are hyperlinks to cited references, still images, and animations. To get the most out of this document, external viewers for JPEG images and QuickTime movies are needed. Each image and animation link has a reference associated with them. This reference provides the title of the particular piece, copyright information, and, in the case of animations, the size of the QuickTime file. You may wish to check the references for animations before downloading them since some are quite large (ranging in size from 28KB to 3.3 MB).


    Distributed Intelligent Agents Group

      This page describes the current research being conducted by DIAG, the Distributed Intelligent Agents Group, at the University of Michigan, Department of Electrical Engineering and Computer Science, Artificial Intelligence Laboratory.

      What Your Computer Really Needs to Know, You Learned in Kindergarten. (Edmund H. Durfee)

      Distributed Problem Solving and Multi-Agent Systems: Comparisons and Examples. (Edmund H. Durfee and Jeffrey S. Rosenschein)

    The Distributed Artificial Intelligence Laboratory

    Rationality in Decision Machines

        ...If an agent [2] has knowledge that one of its actions will lead to one of its goals, then the agent will select that action.

      This formulation can be criticized on several grounds, foremost of which is that it embodies, logical rather than economic rationality (Doyle, 1992). But let us put aside such concerns and consider the perspective that a rationality principle of this general form takes on computing machines. The relevant output identified by this principle is an action selection. Thus, action selections, or in effect, actions, are the products of a knowledge-level machine....

    The CWRU Autonomous Agents Research Group


    Fuzzy Logic FAQ

    What is a Fuzzy Expert System? by Eric Horstkotte

    The Fuzzy Logic Notebook at NASA

    Fuzzy archive by subject


      CLIPS is an expert system shell developed by NASA. The IIT of the NRC has developed FuzzyCLIPS, an enhanced version of CLIPS that supports Fuzzy Logic. FuzzyCLIPS is available without charge.

    Adaptive fuzzy logic using Genetic Algorithms

      Genetic-Adapt FuzzyWare (GAF) allows users to generate a fuzzy control system by simply defining the inputs, outputs, data set, and initial rule sets. GAF uses genetic algorithm to derive proper rules and fuzzy sets from the initial rules. By changing, adding, deleting rules and fuzzy membership sets, the genetic algorithm automatically adapts and optimizes the fuzzy control system.

    Decision/Risk Analysis

    Yahoo Search - Science:Computer Science:Artificial Intelligence:Fuzzy Logic


    Neural Network Information

      The FAQ of the usenet newsgroup

      Neural Network Reports: For each report, both an abstract and the complete report are available!

    Neural Network Hardware (Clark Lindsey at CERN)

    Freeware Neural Network Development Tools


    The Molecular Science Research Center's Neural Network Research Group

    Neural Networks at Pacific Northwest National Laboratory (PNNL)

    Neural Adaptive Control Technology (NACT)

      The Research project is a joint undertaking of Daimler-Benz (Berlin, Germany), Coordinating Partner, and Glasgow University (Glasgow, Scotland, UK). It is a three-year project, which started in April 1994.

      The Project aims at a synergy of adaptive control and neural networks. Similarities of both technologies on an abstract conceptual level constitute a basis for fundamental, engineering orientated research. Basic aspects of the fusion of technologies are investigated in the context of multiple computing agents and industrial automation environments.




      Quick and Simple guide to the Cellular Network [Beginner's Guide]

    Archives of the Cellular Automata Mailing List.

      The archives are stored in the ca.archive* files ie 'ca.archive-1987.Z' holds the compressed archive for the year 1987. The archive for the current year is stored in 'ca.archive'.

Ms. Guidance on Genetic Art