AGENTS-00/ECML-00 Joint Workshop
June 3, 2000
Organizing committee members:
- Peter Stone
(Co-Chair), AT&T Labs -- Research, firstname.lastname@example.org
- Sandip Sen
(Co-Chair), Univ. of Tulsa, email@example.com
- Hans-Dieter Burkhard, Humboldt University, firstname.lastname@example.org
- Jeff Rosenschein, Hebrew University, Israel, email@example.com
- Moshe Tennenholtz, Technion/Stanford University, firstname.lastname@example.org
- Eiji Uchibe, Osaka University, email@example.com
- Jose Vidal, University of South Carolina, firstname.lastname@example.org
Machine learning research has matured into an effective tool at the
disposition of the designer/developer of intelligent agent based systems.
Such agents often work in environments which are at best partially
understood and where the domain characteristics or participants change over
time. In addition, agents can serve their associated 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 in their environments. 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
environments and the requirements of individual agents.
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
We emphasize 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 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
- Benefits of adaptive/learning agents over agents with fixed behavior.
- Evaluation of the effectiveness of individual learning strategies
(e.g., case-based, explanation-based, inductive), or multistrategy
- 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 and constructing algorithms that guarantee convergence
and stability of group behavior.
- Analyzing effects of knowledge acquisition mechanism on
responsiveness of agents or groups to addition/deletion of other
agents from the environment.
- Agents learning via passive or non-intrusive observation of user
- Agents learning to serve its user better by observing other agents
do their job.
- Evolving agent behaviors or co-evolving multiple agents with
- Investigation of teacher-student relationships between the agent and
E-mail the URL of either
to Peter Stone at email@example.com. Papers and
statement of interest must be in one of the following formats: postscript, pdf, HTML.
- a brief statement of interest (1 page), or
- a complete paper (3000 words maximum) including keywords and authors' complete address
Direct correspondence to:
AT&T Labs -- Research
180 Park Ave.
Florham Park, NJ 07932
Deadline for paper submission: March 13, 2000
Acceptance notice to participants: March 27, 2000
Camera-ready papers due: April 10, 2000