A.L.E.S.
From ALESwiki
The Artificial Life Ecosystem Simulator is an interactive enabled Java program. As the name suggests, ALES is a simulation program. A number of different plant and animal species will coexist in an environmental model, and by interacting with the program the user can learn the basic rules of the system.
As in games like SimCity© or SimEarth© the goal of the game is to keep the ecosystem running indefinitely.
| Table of contents |
Introduction
Artificial Intelligence and Artificial Life are not quite the same field. However, both fields are concerned with the same basic question: That of developing systems that are adaptable, that display behaviors that are emergent properties of their programming rather than simple executions of instructions.[2] An adaptable system, however, does not have to be intelligent in the sense of having predictive power or being able to model an evironment. Especially in networking, many researchers are trying to harnass the properties of so called swarm intelligence to solve problems.[1, 6]
A swarm intelligent system is essentially a simple agent based model consisting of a huge number of simple agents interacting with each other and some environment. The behavior of this system as a whole can be used to solve difficult problems, such as TSP.[4] Because this type of system models living systems, such as Ants or Fish, it is Artificial Life. Because the system is an adaptable, agent based system, it is also artificial intelligence.
A.L.E.S.
The Artificial Life Ecosystem Simulator is a Java program that attempts to model simple life forms. Very simple software agents interact with each other and an environment. In the current system, there are three creature models, carnivores, herbivores, and plants. Carnivores are capable of detecting and eating herbivores, herbivores detect and eat plants, and plants just drift. All three species have a lifespan, during which they may divide several times to create new creatures.
In the current A.L.E.S. system, there is no variation between two plants, all plants are exactly the same. So to add a new behavior to the system, the programmer has to create new classes. Without variation, the simulation is completely flat, no new behavior can appear and the system as a whole cannot adapt. So if the environment is altered in some way to be hostile to a certain species, that species will simply go extinct.
So the current A.L.E.S. system is not artificial life or artificial intelligence. It is, however, a promising framework on which to build both.
New Directions for the A.L.E.S
In order for the ALESim to be an intelligent system, it must involve adapation to circumstance. However, the system only models very simple (unicellular) agents. Such creatures, in the real world, show very limited abilities to learn or alter their behavior. However, populations of such creatures show natural variability in characteristics, which, over a great many generations, lead to such phenomena as a species of bacteria "learning" to eat nylon. [3]
The next direction for the ALESim is to create population variation and heredity. This will allow entire populations to learn over time, a crucial step in modeling both intelligence and life. A further step from that would be an attempt to model more complex and intelligent single celled systems, such as the mammalian immune system, which is a complex agent based information processing system, and therefore an excellent subject for artificial intellence research.[5]
However, it would be much more interesting to evolve an immune system than simply design one, so the best direction for the ALESim to take is to maximize the evolvability of the system.
ALife Never Evolves
A shortfall of many artificial life systems is this: no matter how long you run the simulation, there will be no evolution. This is a statement that requires some support. After all, if Fluidiom (http://fluidiom.v2.nl/index.html) creatures do change over time according to a Darwinian process, and this is evolution. However, no matter how long your run fludiom, you will always have the same fitness landscape because there is no interplay between ceatures and no interplay between creatures and the environment. And so you can never get an emergent property, a new level of complexity generated by the system.
Instead, Fludiom evolution is more like evolution in the vertabrate immune system. In the vertebrate immune system, there is a complex interplay between two types of cells, T and B cells, the upshot of which is that B cells produce antibodies at the direction of T cells. These antibodies are specific to particular pathogens that can enter the body, and there is no real upper limit on the number of different antibodies that can be produced, but each B cell produces only one particular antibody.
So, if the body encounters a novel pathogen, a large population of randomly mutated B cells is tested. If any of them produce an antibody that is good for this particular pathogen, that one is selected and directed to produce clones while the pathogen is in the body. A small population of this particular cell remains in the body without reproducing. In this way, vertebrates build up a repetoire of immunities over its lifetime.
There is an emergent property that occurs in the immune system. The entire system can fail and begin attacking the body, for example. But over the course of an individuals lifetime, the immune system, like Fludiom, just produces a increasing population of successful antibody generators.
While this is not a trivial accomplishment, it is not rich enough to mimic the bootstrapping to higher complexity that appears to occur in biological evolution. I have not seen any system that demonstrates this bootstrapping... I suspect strongly that none exists.
Emergent Properties
Section to come.
References
- D. de Oliveira, P. R. F. Jr., and A. L. C. Bazzan, "A swarm based approach for task allocation in dynamic agents organizations," in AAMAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems., Washington, DC, USA: IEEE Computer Society, 2004, pp. 1252--1253.
- J. Doyle and T. Dean, "Strategic directions in artificial intelligence," ACM Comput. Surv., vol. 28, no. 4, pp. 653--670, 1996.
- S. Ohno, "Birth of a unique enzyme from an alternative reading frame of the preexisted, internally repetitious coding sequence." Proc Natl Acad Sci U S A, vol. 81, no. 8, pp. 2421--2425, April 1984.
- P. Tarasewich and P. R. McMullen, "Swarm intelligence: power in numbers," Commun. ACM, vol. 45, no. 8, pp. 62--67, 2002.
- S. Ziane and A. Melouk, "A swarm intelligent multi-path routing for multimedia traffic over mobile ad hoc networks," in Q2SWinet '05: Proceedings of the 1st ACM international workshop on Quality of service & security in wireless and mobile networks New York, NY, USA: ACM Press, 2005, pp. 55--62.
