home

ECoMASS Workshop: Call for Papers

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 EcoMASS workshop, now in its 8th iteration, welcomes original submissions 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

Each accepted paper will be presented orally at the workshop and distributed in the workshop proceedings to all GECCO attendees. Authors should follow the same format as is used for the GECCO conference papers; refer to http://www.sigevo.org/gecco-2014/papers.html for details. Manuscripts should not exceed 8 pages. Papers should be submitted by March 28, 2014 (EXTENDED TO APRIL 7, 2014) in PDF format to forrest.stonedahl@centre.edu with "ECoMASS paper submission" in the subject line.

Important Dates

Paper submission deadline: March 28, 2014 (EXTENDED TO APRIL 7, 2014)

Notification of acceptance: April 15, 2014

Camera-ready deadline: April 25, 2014

Accepted Author Registration deadline: TBA

ECoMASS 2014 Workshop Schedule

Date: July 12, 2014

Time: 8:30-12:30

Location: Port Hardy Room, Sheraton Wall Centre, Vancouver, Canada

Accepted Papers

Dynamic Learning of Heart Sounds with Changing Noise: An AIS-Based Multi-Agent Model Using Systemic Computation; Yiqi Deng, Peter J. Bentley

A Genetic Based Scheduling Approach of Real-Time Reconfigurable Embedded Systems; Hamza Gharsellaoui, Hamadi Hasni, Samir Ben Ahmed

A Study on the Configuration of Migratory Flows in Island Model Differential Evolution; Rodolfo A. Lopes, Rodrigo C. Pedrosa Silva, Alan R. R. Freitas, Felipe Campelo, Frederico G. Guimaraes

The Effect of Communication on the Evolution of Cooperative Behavior in a Multi-Agent System; Sherri Goings, Emily P. M. Johnston, Naozumi Hiranuma

Modeling the Information Propagation in an Email Communication Network Using an Agent-Based Approach; Bin Jiang, Lei Wang, Chao Yang, Shuming Peng, Renfa Li

Schedule of Presentations

8:30 Welcome and Introductions

8:50 A Genetic Based Scheduling Approach of Real-Time Reconfigurable Embedded Systems; Hamza Gharsellaoui, Hamadi Hasni, Samir Ben Ahmed

9:20 A Study on the Configuration of Migratory Flows in Island Model Differential Evolution; Rodolfo A. Lopes, Rodrigo C. Pedrosa Silva, Alan R. R. Freitas, Felipe Campelo, Frederico G. Guimaraes

9:50 The Effect of Communication on the Evolution of Cooperative Behavior in a Multi-Agent System; Sherri Goings, Emily P. M. Johnston, Naozumi Hiranuma

10:20 Coffee Break

10:40 Modeling the Information Propagation in an Email Communication Network Using an Agent-Based Approach; Bin Jiang, Lei Wang, Chao Yang, Shuming Peng, Renfa Li

11:10 Dynamic Learning of Heart Sounds with Changing Noise: An AIS-Based Multi-Agent Model Using Systemic Computation; Yiqi Deng, Peter J. Bentley

11:20 Research Slam

Automated Generation of Environments to Test the General Learning Capabilities of AI Agents

Oliver Coleman, Alan D. Blair, Jeff Clune

Generational Neuro-Evolution: Restart and Retry for Improvement

David Peter Shorten, Geoffrey Stuart Nitschke

Overcoming Deception in Evolution of Cognitive Behaviors

Joel Lehman, Risto Miikkulainen

Encouraging Creating Thinking in Robots Improves Their Ability to Solve Challenging Problems

Jingyu Li, Jed Storie, Jeff Clune

The Evolution of Kin Inclusivity Levels

Anya Johnson, Heather J. Goldsby, Sherri Goings, Charles Ofria

Evolution of Communication and Cooperation

Jason Fairey, Terence Soule

Coevolutionary Learning of Swarm Behaviors Without Metric

Wei Li, Melvin Gauci, Roderick Gross

Novelty Search Creates Robots with General Skills for Exploration

Roby Velez, Jeff Clune

Evolutionary Agent-Based Simulation of the Introduction of New Technologies in Air Traffic Management

Logan Yliniemi, Adrian K. Agogino, Kagan Tumer

Evolving Neural Networks that Are Both Modular and Regular: HyperNeat Plus the Connection

Cost Technique

Joost Huizinga, Jeff Clune, Jean-Baptiste Mouret

Evolvability is Inevitable

Joel Lehman, Ken Stanley

12:20 Wrap-up and Final Discussion

12:30 End of Workshop

Program Committee

Workshop Chairs

• Bill Rand, University of Maryland
• Forrest Stonedahl, Centre College

Program Committee for ECoMASS 2014

• Emily Zechman Berglund, North Carolina State University
• Mitchell Colby, Oregon State University
• Sherri Goings, Carleton College
• Matt Knudson, Carnegie Mellon University
• Rinde van Lon, KU Leuven
• Michael North, Argonne National Laboratory
• Jim Reggia, University of Maryland
• Robert G. Reynolds, Wayne State University
• Rick Riolo, University of Michigan
• Logan Yliniemi, Oregon State University
• Tina Yu, Memorial University of Newfoundland
• Moshe Sipper, Ben-Gurion University

Former Program Committee Members

• Liviu Panait, Google
• Kagan Tumer, Oregon State University

Former ECoMASS Chairs

• Rick Riolo, University of Michigan
• Sevan G. Ficici, Natural Selection

Previous ECoMASS Workshop Websites

• ECoMASS 2013 Site
  • Earlier years workshop pages currently unavailable...