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Evolutionary Computation and Multi-Agent Systems and Simulation Workshop


  • FIRST CALL FOR PAPERS - FIRST CALL FOR PAPERS -
                         WORKSHOP ON
         Evolutionary Computation and Multi-Agent Systems
              and Simulation Workshop (ECoMASS-2008)
    
                     to be held as part of the
    

2009 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2009)

              July 8-12, 2009 (Wednesday-Saturday)
                   Delta Centre-Ville Hotel
                   Montreal, Canada
                   Organized by ACM SIGEVO
                   www.sigevo.org/GECCO-2009/

           PAPER SUBMISSION DEADLINE FOR WORKSHOP: MARCH 25, 2009

Workshop URL: http://www.cscs.umich.edu/ecomass/


Evolutionary computation (EC) and multi-agent systems and simulation
(MASS) both involve populations of agents. EC is a learning technique
by which a population of individual agents adapt according to the
selection pressures exerted by an environment; MASS seeks to
understand how to coordinate the actions of a population of (possibly
selfish) autonomous agents that share an environment so that some
outcome is achieved. Both EC and MASS have top-down and bottom-up
features. For example, some aspects of multi-agent system engineering
(e.g., mechanism design) are concerned with how top-down structure can
constrain or influence individual decisions. Similarly, most work in
EC is concerned with how to engineer selective pressures to drive the
evolution of individual behavior towards some desired goal. Multi-agent
simulation (also called agent-based modeling) addresses the bottom-up
issue of how collective behavior emerges from individual action.
Likewise, the study of evolutionary dynamics within EC (for example in
coevolution) often considers how population-level phenomena emerge from
individual-level interactions. Thus, at a high level, we may view EC and
MASS as examining and utilizing analogous processes. It is therefore
natural to consider how knowledge gained within EC may be relevant to
MASS, and vice versa; indeed, applications and techniques from one field
have often made use of technologies and algorithms from the other field.
Studying EC and MASS in combination is warranted and has the potential
to contribute to both fields.

The goal of this workshop is to facilitate the examination and
development of techniques at the intersection of evolutionary
computation and multi-agent systems and simulation.

The ECoMASS workshop welcomes original submissions in the theory and
practice on all aspects of Evolutionary Computation and Multi-Agent
Systems and Simulation, which include (but are not limited to) the
following topics and themes:

-Multi-agent systems and agent-based models utilizing evolutionary
computation
-Optimization of multi-agent systems and agent-based models using
evolutionary computation
-Evolutionary computation models which rely not on explicit fitness
functions but rather implicit fitness functions defined by the
relationship to other individuals / agents
-Applications utilizing MASS and EC in combination
-Biological agent-based models (usually called individual-based
models) involving evolution
-Evolution of cooperation and altruism
-Genotypic representation of the complex phenotypic strategies of MASS
-Evolutionary learning within MASS (including Baldwinian learning and
phenotypic plasticity)
-Emergence and feedbacks
-Open-ended strategy spaces and evolution
-Adaptive individuals within evolving populations

*Paper Submission
See http://www.cscs.umich.edu/ecomass/ for details.

*Important Dates
Paper submission deadline: 25 March, 2009
Notification of acceptance: 3 April, 2009

*Workshop Chairs:
Bill Rand, University of Maryland
Sevan Ficici, Natural Selection
Rick Riolo, University of Michigan

*Program Committee:
Matt Knudson, Oregon State University
Mike North, Argonne National Laboratory
Liviu Panait, Google
Bob Reynolds, Wayne State University
Moshe Sipper, Ben-Gurion University
Kagan Tumer, Oregon State University
Tina Yu, Memorial University of Newfoundland

Discussion

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