A. Qureshi. Research Note, RN/96/4. UCL, Gower Street, London, WC1E 6BT, UK, (January 1996)
Abstract
The paradigm of agent based computing is becoming
increasingly popular both in distributed artificial
intelligence and as a general software engineering
technique. The difficulty with agent based computing is
that success depends not on the correctness of any one
agent, but on the emergent behaviour arising from the
interaction of a society of agents. As a consequence,
the problem of programming agents is non trivial and
poorly understood.
In this paper we show that genetic programming can be
used to automatically program agents which communicate
and interact to solve problems. The programs evolved
simulataneously define when and what to communicate,
and how to use the communicated information to solve
the given problem.
%0 Report
%1 qureshi:1996:eaRN
%A Qureshi, A.
%C Gower Street, London, WC1E 6BT, UK
%D 1996
%K (ADF) AI, Agent Artificial Automatic Automatically Based Code Communication, Computing, Defined Distributed Evolution, Functions Generation, Learning, Machine Programming, algorithms, genetic programming,
%N RN/96/4
%T Evolving Agents
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/AQ.gp96.ps.gz
%X The paradigm of agent based computing is becoming
increasingly popular both in distributed artificial
intelligence and as a general software engineering
technique. The difficulty with agent based computing is
that success depends not on the correctness of any one
agent, but on the emergent behaviour arising from the
interaction of a society of agents. As a consequence,
the problem of programming agents is non trivial and
poorly understood.
In this paper we show that genetic programming can be
used to automatically program agents which communicate
and interact to solve problems. The programs evolved
simulataneously define when and what to communicate,
and how to use the communicated information to solve
the given problem.
@techreport{qureshi:1996:eaRN,
abstract = {
The paradigm of agent based computing is becoming
increasingly popular both in distributed artificial
intelligence and as a general software engineering
technique. The difficulty with agent based computing is
that success depends not on the correctness of any one
agent, but on the emergent behaviour arising from the
interaction of a society of agents. As a consequence,
the problem of programming agents is non trivial and
poorly understood.
In this paper we show that genetic programming can be
used to automatically program agents which communicate
and interact to solve problems. The programs evolved
simulataneously define when and what to communicate,
and how to use the communicated information to solve
the given problem.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Gower Street, London, WC1E 6BT, UK},
author = {Qureshi, A.},
biburl = {https://www.bibsonomy.org/bibtex/2ff1c795cd62b81d4e620a82849adc3e1/brazovayeye},
institution = {UCL},
interhash = {936854a0cdbdd044405ab46af69f4c11},
intrahash = {ff1c795cd62b81d4e620a82849adc3e1},
keywords = {(ADF) AI, Agent Artificial Automatic Automatically Based Code Communication, Computing, Defined Distributed Evolution, Functions Generation, Learning, Machine Programming, algorithms, genetic programming,},
month = {January},
notes = {Submitted to GP96},
number = {RN/96/4},
size = {10 pages},
timestamp = {2008-06-19T17:49:59.000+0200},
title = {Evolving Agents},
type = {Research Note},
url = {http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/AQ.gp96.ps.gz},
year = 1996
}