AGENTS-2001 Workshop on Learning Agents May 29,2001 Montreal, Canada **************************************** Submission Deadline: March 16th, 2001 **************************************** Description ----------- Intelligent agents often work in environments which are at best partially understood and where the domain characteristics or participants change over time. Under such circumstances, learning and adaptation are key for obtaining good performance. In addition, agents can serve their associate users much more effectively if they are able to capture the unspecified and/or changing preferences of these users. Another aspect of agent based systems is that they often are situated in a multiagent environment. Agents in such systems have to interact both with associated users and other agents. Coordination of the activities of multiple agents, whether selfish or cooperative, is essential for the viability of any system in which multiple agents must coexist. Learning and adaptation are invaluable mechanisms by which agents can evolve coordination strategies that meet the demands of the environment and the requirements of individual agents. The goal of this workshop is to focus on research addressing the unique requirements that autonomous agents impose on learning methods. The Learning Agents workshop organized jointly at the Agents'2000 and ECML'2000 conferences started a fruitful discussion between researchers involved in designing and applying machine learning techniques to autonomous agents. The workshop attracted more than 40 researchers studying learning agents. The workshop ended with a lively discussion regarding the special properties of agent learning as opposed to machine learning in general. The proposed workshop aims to continue and extend the discussion started last year. We especially encourage the submission of papers addressing the following topics: 1) Benefits of adaptive/learning agents over agents with fixed behavior. 2) Evaluation of the effectiveness of individual learning strategies (e.g., case-based, explanation-based, inductive, reinforcement learning), or multistrategy combinations. 3) Characterization of learning and adaptation methods in terms of modeling power, communication abilities, knowledge requirements and processing abilities of individual agents. 4) Developing learning and adaptation strategies, or reward structures, for environments with cooperative agents, selfish agents, partially cooperative agents (agents that will cooperate only if individual goals are not sacrificed) and for environments that can contain mixture of these types of agents. 5) Analyzing and constructing algorithms that guarantee the convergence and stability of group behavior in multi-agent systems. 6) Analyzing the effects of knowledge acquisition mechanisms on the responsiveness of agents or groups to the addition/deletion of other agents from the environment. 7) Agents learning by observing users or other agents. 8) Evolving agent behaviors or co-evolving multiple agents with similar/opposing interests. 9) Investigation of teacher-student relationships between agents and users. Submission Requirements: ----------------------- E-mail the URL of either a -- brief statement of interest (1 page), -- complete paper (3000 words maximum) including keywords and authors' complete address to pstone@research.att.com and dprecup@cs.mcgill.ca. Papers and statements of interest must be in one of the following formats: postscript, pdf, HTML. Direct all questions and inquiries to: Doina Precup (Co-chair) School of Computer Science McGill University 3480 University st. Montreal, Quebec, Canada H2A 1A7 (514) 398-6443 (514) 398-3883 (fax) dprecup@cs.mcgill.ca http://www.cs.mcgill.ca/~dprecup Important Dates --------------- Deadline for paper submission: March 16, 2001 Acceptance notice to participants: April 1, 2001 Camera-ready papers due: April 16, 2001 Workshop: May 29, 2001 Organizing Committee #################### Doina Precup (Co-Chair), McGill University, dprecup@cs.mcgill.ca Peter Stone (Co-Chair), AT&T Labs -- Research, pstone@research.att.com Hans-Dieter Burkhard, Humboldt University, hdb@informatik.hu-berlin.de Amy Greenwald, Brown Univeristy, amygreen@cs.brown.edu Tim Oates, MIT AI Lab, oates@ai.mit.edu Enric Plaza, IIIA-Spanish Scientific Research Council, enric@iiia.csic.es Sandip Sen, University of Tulsa, sandip@kolkata.mcs.utulsa.edu Kagan Tumer, NASA Ames Research Center, kagan@ptolemy.arc.nasa.gov Moshe Tennenholtz, Technion/Stanford University, moshe@robotics.stanford.edu Eiji Uchibe, Osaka University, uchibe@er.ams.eng.osaka-u.ac.jp Manuela Veloso, Carnegie Mellon University, veloso@cs.cmu.edu Jose Vidal, University of South Carolina, vidal@sc.edu For further information visit the following URL: http://www.research.att.com/~pstone/Workshops/2001agents/ =============================================================================