Learning and Evolution in Agent Based Systems
July 20, 2004
Organizing committee members:
Sen (Chair), Univ. of Tulsa, email@example.com
Oliveira, Faculdade de Engenharia Universidade do Porto, eco at fe.up.pt
- Enric Plaza, IIIA,
Artificial Intelligence Research Institute, enric at iiia.csic.es
- Peter Stone,
University of Texas, Austin, pstone at cs.utexas.edu
Front material from workshop proceedings including presentation
- Stephane Airiau, Nilanjan Ganguly, Sandip Sen, Sabyasachi Saha,
"Evolutionary tournament-based comparison of learning and non-learning
strategies for iterated games,"
- Teddy Candale & Sandip Sen,
"Choosing satisficing service providers by learning referrals"
- Jacob Crandall and Michael A. Goodrich,
"Establishing Reputation Using Social Committment in Repeated Games"
- Partha S. Dutta, Srinandan Dasmahapatra, Steve R. Gunn, Nicholas
R. Jennings, and Luc Moreau,
"Strategic Communication to Improve Distributed Learning"
- Vanessa Frias-Martinez and Elizabeth Sklar,
"A team-based co-evolutionary approach to multi agent learning"
- Matthew E. Gaston, John Simmons, and Marie DesJardines,
"Learning to Form Teams in Networks"
- Yoav Horman and Gal A. Kaminka,
"Improving Sequence Learning for Modeling Other Agents"
- Sebastien Paquet, Nicolas Bernier and Brahim Chaib-draa,
"Selective Perception Learning for Tasks Allocation"
- Avi Rosenfeld, Gal A Kaminka, and Sarit Kraus,
"Adaptive Robot Coordination using Interference Metrics"
Statement of Interest
- Myriam Abramson and Ranjeev Mittu,
"Joint Intentions and Joint Actions: Closing the Loop"
- Xin Li, Qingping Tao, and Leen-Kiat Soh,
"Learning Negotiation Approach Selection with SVM"
Researchers in machine learning and adaptive systems have been
addressing issues concerned with learning and adapting from past
experience, observation, failures, etc. Whereas most of this research
have focused on techniques for acquisition and effective use of problem
solving knowledge from the viewpoint of a single autonomous agent,
recent investigations have opened the possibility of application of
some of these techniques in multiagent settings.
The goal of this workshop is to focus on research that will address unique
requirements for agents learning and adapting to their environment.
Recognizing the applicability and limitations of current machine learning
research when applied to situated agents will be of particular relevance to
The workshop will also encourage presentation and discussion of ideas
relating to evolutionary learning and adaptation techniques in the
context of agent based systems. Expected contributions include both
evolutionary learning by individual agents as well as evolutionary
design of agent societies. Of particular interest is new models for
coevolving agent populations.
We solicit research contributions that address new learning
modalities, e.g., the use of communication to enhance learning.
Presentation of applications of learning in key multiagent problems
like negotiation, teamwork, trust, auctions, supply chains, etc. are
We focus on three different ways in which machine learning can be used
to enhance the performance of an Agent Based System:
- An agent can learn the preferences and changing priorities of associated
- An agent can learn about other agents in the environment in
order to compete and/or cooperate with them. An agent can learn from
other agents, taking advantage of their experiences and incorporating
these into its own knowledge base.
- An agent can learn about other regularities in its environment.
Topics of interest
We would particularly welcome new insights into these problems from
other related disciplines and thus would like to emphasize the
inter-disciplinary nature of the workshop. Among others, papers of
the following kind are welcome:
- Evaluation of the effectiveness of individual learning strategies
(e.g., case-based, explanation-based, inductive, reinforcement), or
- Characterization of learning and adaptation methods in terms of
modeling power, communication abilities, knowledge requirement,
processing abilities of individual agents.
- Developing learning and adaptation strategies, or reward
structures, for environments with cooperative agents, selfish
agents, partially cooperative (will cooperate only if individual
goals are not sacrificed) and for environments that can contain
mixture of these types of agents.
- Analyzing convergence properties of existing algorithms and
constructing algorithms that guarantee convergence and stability of
- Evaluating effects of knowledge acquisition mechanisms on
responsiveness of agents or groups to changes in the agent
population in the environment.
- Learning to work as an effective team by taking advantage of
complementary skills and resources.
- Agents learning via passive or non-intrusive observation of user
behaviors or by mimicking other agents.
- Evolving agent behaviors or co-evolving multiple agents with
- Investigation of teacher-student relationships between agents or
between an agent and the associated user.
- Applications of learning agents including agents that learn to
negotiate contracts, learning trustworthiness of other agents,
learn to detect security threats, etc.
Prospective authors should inform the Primary Contact on the organizing
committe of their intent to submit a paper by April 9, 2004.
E-mail the URL of either
to Sandip Sen at firstname.lastname@example.org. Papers and
statement of interest must be either in postscript or pdf. NOTE: You should send an URL for retrieving the
papers, not the actual paper (if this is a problem, please contact Sandip
Sen at firstname-lastname at utulsa.edu).
- a brief statement of interest (1 page), or
- a complete paper (3000 words maximum) including keywords, word count, and authors' complete address
- Intent to submit: April 9. 2004
- Deadline for paper submission:
Due to administrative requirements we strongly recommend submission by April 12, 2004
though we will consider submissions, with prior approval, until
April 19, 2004
- Acceptance notice to participants: May 1, 2004
- Camera-ready papers due: May 24, 2004