AAMAS-04 Workshop
Learning and Evolution in Agent Based Systems
July 20, 2004
New York

Organizing committee members:

Front material from workshop proceedings including presentation schedule: ps pdf,

Accepted Papers

Statement of Interest


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 this workshop. 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 welcome. We focus on three different ways in which machine learning can be used to enhance the performance of an Agent Based System:

  1. An agent can learn the preferences and changing priorities of associated users.
  2. 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.
  3. 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:

Submission Requirements

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 sandip-sen@utulsa.edu. 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).

Important Dates

Sandip Sen's homepage