Artificial Life Primitives

The nanoScheme language includes also a reduced set of bio-inspired primitives. They have

been designed in the same spirit of Tom Ray’s Tierran assembly language (Ray, 1991). That

is, the production of synthetic organisms based on a computer metaphor of organic life in

which CPU time is the “energy'” resource and memory is the “material” resource. This set

includes the following functions:

 create – create a new execution environment.

 reproduce - create a new nano-agent in the local environment.

 terminate - kill the current nano-agent.

 diffuse - diffuse a message to other nano-agents in the local environment.

 send – send a message to a distant execution environment.

 rule - define a behavior rule consisting of condition, action and priority

expressions.

 engine - make an inference loop on a behavior rule base.

 crossover - genetic programming crossover operator.

 mutate - genetic programming mutate operator and random code generator.

 random - return a random real number.

 time - return the current real time.

 stress - return a “stress” value based on the current available memory and

computing resources.

 plugin - dynamically load a new package of dedicated primitive functions.

 message – hook function invoked when the nano-agent receives a message.

 l ifepulse - hook function for implementing periodic behaviors.

Since all code, behavior rules, and messages are basically S-expressions (i.e. lisp

expressions), the use of genetic programming is natural in this environment (Koza, 1992). As

an example, the next code illustrates the use of the mutate primitive:

(mutate '(/ y 2) '((+ 2) (- 2)) ' (􀗛 2 3) 3)  (/ y (+ 􀗛 (- 􀗛 2)))

The mutate function applies a mutation on a Scheme program expression (first argument). It

creates a randomly generated program with a maximum depth (last argument). Functions

and terminals are randomly chosen in two lists (arguments 2 and 3). The generated program

replaces a randomly chosen “node site” in the expression. If the first argument is the empty

list, then the mutate operator returns a new random expression.

Note that the remote execution of code on distant nano-agents is a natural feature of the

nanoScheme language by simply diffusing or sending messages containing S-expressions.

These expressions are then evaluated by all nano-agents. This approach enables an easy

implementation of distributed algorithms on nano-agents.