From fil@cs.ucsd.edu Wed Mar 2 18:46:38 EST 1994 Article: 2360 of comp.ai.genetic Xref: glinda.oz.cs.cmu.edu comp.ai.genetic:2360 Path: honeydew.srv.cs.cmu.edu!fs7.ece.cmu.edu!europa.eng.gtefsd.com!howland.reston.ans.net!gatech!swrinde!ihnp4.ucsd.edu!network.ucsd.edu!sdcc12!cs!fil From: fil@cs.ucsd.edu (Filippo Menczer) Newsgroups: comp.ai.genetic Subject: LEE V1.1 ALife Model/Simulator Message-ID: <62047@sdcc12.ucsd.edu> Date: 23 Feb 94 03:44:25 GMT Sender: news@sdcc12.ucsd.edu Organization: CSE Dept., U.C. San Diego Lines: 175 Nntp-Posting-Host: billy.ucsd.edu ========================== LEE release 1.1 Latent Energy Environments ========================== The LEE artificial life simulator is available via public ftp. This is the latest release: the code is under continuous development, so further stable releases will be made available in the future at the same site. You may download the software (Unix/Mac souces and/or executables, documentation, and a technical report) as follows: ftp cs.ucsd.edu (132.239.51.3) login: anonymous password: your_email_address cd pub/LEE get ... bye Filename Format Content ---------------------------------------------------------------- README ASCII general info lee.doc ASCII documentation pinep.ps.Z compressed PostScript LEE model/results paper lee11.Unix.sh ASCII shar archive LEE 1.1 Unix source lee11.Mac.sh mixed shar archive LEE 1.1 Mac add'l source lee11exe.Mac.sh binhexed shar archive LEE 1.1 Mac executables ---------------------------------------------------------------- Please read README for general information, and lee.doc for specific information on how to compile and run the program. To get the PostScript paper, use the Unix utility 'uncompress'. To unpack the source and/or Mac executables you must use the Unix utility 'unshar'. After this, to get the binary Mac executables and/or resource file, use, eg, 'BinHex 4.0'. LEE is (c) University of California, San Diego. Authors: Richard Belew and Filippo Menczer (Cognitive Computer Science Research Group, CSE Dept, UC San Diego). Please send important comments, suggestions, and bugs to the latter (fil@ucsd.edu). You may freely copy/distribute the software, except for commercial purposes, and as long and the notices in the source headers are preserved. Other contributions to the code are from: Stefano Nolfi and Jeff Elman; Greg Linden (Mac interactive version); and Federico Cecconi (sensory system). OVERVIEW OF LEE (from lee.doc) ============================== LEE (Latent Energy Environments) is both an Alife model and a software tool to be used for simulations within the framework of that model. We hope that LEE will help us understand a broad range of issues in theoretical, behavioral, and evolutionary biology. The LEE tool described here consists of approximately 7,000 lines of C code and runs in both Unix and Macintosh platforms. The modeling of environmental complexity across different Alife experiments is perhaps the main motivation behind this project. LEE allows the specification of environments of graduated complexity. A spacially distributed series of "atomic elements" must be combined to transform their "latent potential energy" into "work" necessary for survival. Behavioral strategies must be evolved by the population such as to allow an efficient exploitation of the available energy. This latent energy can be used to measure the environment complexity with respect to the survival task. A steady-state genetic algorithm is used in the LEE model rather then a lock-step generational one. The progression of the adaptive process is measured in terms of time rather than generations. At any one time step possibly all the organisms in the population may live, use and/or acquire energy, and reproduce or die. Consequently, the size of the population varies with time. If latent energy is not made available at a rate sufficient to support the energy expense of the population, extintion may occur. An organism is implemented by a feed-forward neural network plus a sensory-motor system and a gut, i.e. a reservoir for energy, both in work (usable) and latent (atomic elements) form. The sensory system consists of a user-specified set of sensors that are mapped onto the network input units. The network may have as many hidden layers as desired. The output layer maps its activation values onto the motor system, made of a set of user-specified motors. Learning can occur in the current version by means of standard back-propagation of error. The error is computed on an input prediction task. Each organisms lives by moving in a world consisting of a rectangular grid with toroidal edge conditions. Each basic life cycle (sweep) consists of 5 steps: 1. Gather information about the surrounding world by means of a set of sensors. 2. Elaborate the sensory information to produce a motor action. 3. Make a movement in the world by means of a set o motors. 4. (Optional) Use the new sensory information as teaching input for a prediction task learned during an organism's lifetime on a subset of the neural net. 5. Consequences of the movement: there is an energy cost, there may be an energy increase or decrease (depending on the contents of the new world position and the reactions caused by the acquisition of such contents), and finally these energy changes may result in death or reproduction. Different sensor systems implemented in the current version are: GUT, CONTACT, and AMBIENT. The first senses elements present in an organism's own gut; the second senses those present in the world cell in front of the position currently occupied by the organism; the third senses those present in a local range, weighed according to their distange in number of steps. Each sensor has a complex that identifies which elements can be sensed by it. There is one simple motor system currently implemented: BINARY. It allows the organism to make one of four possible moves: stay still, turn left or right 90 degrees, or move ahead. Each motor has a power that specifies how far the organism can be moved by it. LIST OF RELATED PAPERS (as of February 1994) ============================================ Menczer F and Belew RK 'Latent Energy Environments: A Model for Artificial Life Complexity' Technical Report CS93-298, July 1993, University of California, San Diego Menczer F and Belew RK 'Latent Energy Environments: A Tool for Artificial Life Simulations' Technical Report CS93-301, July 1993, University of California, San Diego Menczer F 'Changing Latent Energy Environments: A Case for the Evolution of Plasticity' Technical Report CS94-336, January 1994, University of California, San Diego (*) Menczer F and Belew RK 'Latent Energy Environments' to appear in "Plastic Individuals in Evolving Populations", Santa Fe Institute Studies in the Sciences of Complexity, Addison-Wesley (*) This paper is available on the LEE ftp site as 'pinep.ps.Z'. An abstract follows: A novel ALife model and simulator, called LEE, is introduced and described. The motivation lies in the need for a measure of complexity across different ALife experiments. This goal is achieved through a careful characterization of environments in which different forms of energy are well-defined and conserved. A steady-state genetic algorithm is used to model the evolutionary process. Organisms in the population are modeled by neural networks with non-Lamarckian learning during life. Behaviors are shown to be crucial in the interactions between organisms and their environment. The flexibility of LEE for the study of a variety of problems related to complex evolutionary systems is illustrated by some general emerging properties of the model, and by preliminary results of a number of experiment currently under way. === Filippo Menczer and Richard K. Belew Cognitive Computer Science Research Group Dept. of Computer Science and Engineering, 0114 University of California, San Diego La Jolla, CA 92093-0114 USA Fax: (619)534-7029 Email: fil@ucsd.edu === -- ======================================================================= Filippo Menczer /~~~~\ Viva l'Italia, l'Italia che e' in mezzo al mare \_ / l'Italia dimenticata e l'Italia da dimenticare fil@ucsd.edu \ \ l'Italia meta' giardino e meta' galera Article 2364 of comp.ai.genetic: Xref: glinda.oz.cs.cmu.edu comp.ai.genetic:2364 Path: honeydew.srv.cs.cmu.edu!nntp.club.cc.cmu.edu!news.mic.ucla.edu!library.ucla.edu!agate!howland.reston.ans.net!cs.utexas.edu!uunet!Germany.EU.net!Informatik.Uni-Dortmund.DE!home!heitkoet From: heitkoet@home.informatik.uni-dortmund.de (Joerg Heitkoetter) Newsgroups: comp.ai.genetic Subject: Re: LEE V1.1 ALife Model/Simulator Date: 23 Feb 1994 14:36:53 GMT Organization: CS Department, Dortmund University, Germany Lines: 15 Sender: heitkoet@home (Joerg Heitkoetter) Distribution: world Message-ID: <2kfpm5$aji@fbi-news.informatik.uni-dortmund.de> References: <62047@sdcc12.ucsd.edu> NNTP-Posting-Host: home.informatik.uni-dortmund.de In article <62047@sdcc12.ucsd.edu>, fil@cs.ucsd.edu (Filippo Menczer) writes: |> Filename Format Content |> ---------------------------------------------------------------- |> README ASCII general info |> lee.doc ASCII documentation |> pinep.ps.Z compressed PostScript LEE model/results paper |> lee11.Unix.sh ASCII shar archive LEE 1.1 Unix source |> lee11.Mac.sh mixed shar archive LEE 1.1 Mac add'l source |> lee11exe.Mac.sh binhexed shar archive LEE 1.1 Mac executables |> ---------------------------------------------------------------- LEE's also avail. gzip'ed from SAFIER sfi.santafe.edu (192.12.12.1) in /pub/EC/EA/src/LEE as shown above; the paper itself is avail. as /pub/EC/EA/papers/lee94.ps.gz Regards, -joke 250----- Latent Eenergy Environments simulator ---- 250- 250-LEE is (c) University of California, San Diego. 250-You may freely download the software, except for 250-commercial purposes. 250- 250-Problems: email fil@ucsd.edu (Filippo Menczer). 250- 250-Refer to the file lee.doc (text) for 250-specific documentation on the LEE software. 250-