CFP: Learning and Adaption in Multi-Agent Systems (LAMAS) @ AAMAS 2005

[Sander Bohte]

**Apologies for multiple copies**
____________________________________________________________ 

-- CALL FOR PAPERS -- 
-- Workshop on Learning and Adaptation in Multi-Agent Systems 2005 (LAMAS) --  
-- To be held at AAMAS 2005, Utrecht University, the Netherlands --
-- http://lamas2005.luc.ac.be --
____________________________________________________________ 
  
Dear Connectionists,

you are invited to submit papers to the 1st Workshop  on 
Learning and Adaption in MAS (LAMAS 2005).

LAMAS 2005 will be organized within the fourth International Conference on
Autonomous Agents and Multi Agent Systems in Utrecht, the Netherlands.
Prospective participants must also register for the AAMAS 2005 conference. The
number of participants is strictly limited.

The goal of this workshop is to increase awareness and interest in adaptive
agent research, encourage collaboration between ML experts and agent system
experts, and give a representative overview of current research in the area of
adaptive agents.

Machine Learning techniques for single agent frameworks are well established.
Agents operate in uncertain environments and must be able to learn and act
autonomously. This task is however more complex when the agent interacts with
other agents with potentially different capabilities and goals. The single
agent case is structurally different from the multi agent case due to the
added dimension of dynamic interactions between the adaptive agents. 

Multi-Agent Learning, i.e., the ability of the agents to learn how to
co-operate and compete, becomes crucial in many domains. Adaptive Agents and
Multi-Agent Systems (AAMAS) is an emerging multi-disciplinary area
encompassing Computer Science, Software Engineering, Biology, as well as
Cognitive and Social Sciences. A theoretical framework, in which rationality
of learning and interacting agents can be understood, is still under
development in MASs, although there have been promising first results. 

We invite contributions that cover on how an agent can learn using ML
techniques to act individually, and/or to coordinate with one another towards
individual or common goals. This is an open issue in real-time, noisy,
collaborative and adversarial environments.

We interpret ML techniques in a broad context. These can include the non
exhaustive list of Reinforcement Learning, Genetic Algorithms, Neural Networks
or Evolutionary Game Theoretic approaches to learning. Also of interest are
models for coevolving agent populations. Key-applications, where these
techniques can be applied, for example, consist of load balancing problems,
traffic management, teamwork, trust, auctions, supply chains, etc.

We consider three possible ways in which machine learning can be used to
enhance the application 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. An agent can also learn almost selfishly and have limited
communication with other agents.
3. An agent can learn about other regularities in its environment.

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:
1. Evaluation of the effectiveness of individual learning strategies  (e.g.,
case-based, explanation-based, inductive, reinforcement), or  multi strategy
combinations.
2. Characterization of learning and adaptation methods in terms of  modeling
power, communication abilities, knowledge requirement,  processing abilities
of individual agents. For instance through  the use of Game Theoretic models.
3. 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.
4. Analyzing convergence properties of existing algorithms and  constructing
algorithms that guarantee convergence and stability of  group behavior.
5. Evaluating effects of knowledge acquisition mechanisms on  responsiveness
of agents or groups to changes in the agent  population in the environment.
6. Learning to work as an effective team by taking advantage of  complementary
skills and resources.
7. Agents learning via passive or non-intrusive observation of user  behaviors
or by mimicking other agents.
8. Evolving agent behaviors or co-evolving multiple agents with
similar/opposing interests.
9. Investigation of teacher-student relationships between agents or  between
an agent and the associated user.
10. Applications of learning agents including agents that learn to  negotiate
contracts, learning trustworthiness of other agents,  learn to detect security
threats, etc.

Those wishing to present should (electronically) submit a full-scale paper,
not longer than 16 pages (references and figures included) to Karl Tuyls
(karl.tuyls at luc.ac.be) or Katja Verbeeck (kaverbee(at)vub.ac.be).

The deadline for submission of contribution is March 14th, 2005. All
contributions will be reviewed and in case of acceptance published in the
workshop proceedings of the AAMAS'05 conference. Authors should submit full
papers electronically in PS or PDF format. In addition, authors should submit
an ASCII abstract, with the following information: title of paper; names and
affiliations of authors; name, email, snail mail, phone number, and fax number
of primary contact; abstract. The same information should be included on the
first page of submitted papers. Papers must be written in English, with a
maximum length of 16 pages. Please format papers according to the LNCS/LNAI
style, a  LaTex class is available at 
http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-164-2-72376-0,00.html
All correspondence will be with the specified primary contact.

Post proceedings of selected and revised papers are to be published as a
Springer Lecture Notes in Artificial Intelligence.
____________________________________________________________ 
IMPORTANT DATES: 

Deadline for Submission of Contributions: March 14th, 2005

Notification of Acceptance/Rejection: april 18th, 2005

Camera Ready Copy of Papers: may 15th, 2005

Workshop Date: 25th or 26th of July 2005, precise date to be announced.
____________________________________________________________ 


Furthermore, if you have any inquiry please do not hesitate to contact the organisers.

____________________________________________________________ 
Organizing Committee:

Karl Tuyls (Primary Contact) 
karl.tuyls at luc.ac.be
LUC Theoretical Computer Science Group
 
Pieter Jan 't Hoen 
hoen at cwi.nl
Evolutionary Systems and Applied Algorithmics

Sandip Sen 
firstName-lastName at utulsa.edu
Department of Mathematical and Computer Sciences

Katja Verbeeck 
kaverbee(at)vub.ac.be
Computational Modeling Lab
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Program Committee:

Stephane Airiau, Department of Mathematical & Computer Sciences, The University of Tulsa, USA
Bikramjit Banerjee, Department of Computer Science, University of Tulane, USA
Ana Lucia Bazzan, Institute of Informatics, Universidade Federal do Rio Grande do Sul, Brazil
Sander Bohte, CWI, Evolutionary and Applied Algorithmics group, The Netherlands
Michael Goodrich, Department of Computer Science, Brigham Young University, USA
Daniel Kudenko, Department of Computer Science, University of York, UK
Han La Poutre, CWI, Evolutionary and Applied Algorithmics group, The Netherlands
Michael Littman, Rutgers University, Department of Computer Science, USA
Peter McBurney, Biocomputing and Computational Biology Group, Liverpool, UK
Ann Nowe, Computational Modeling Lab, Vrije Universiteit Brussel, Belgium
Simon Parsons, Department of Computer and Information Science, Brooklyn College, New York, USA.
Steve Phelps, Biocomputing and Computational Biology Group, Liverpool, UK
Jan Ramon, KULeuven, DTAI group, Department of Computer Science, Belgium
Peter Stone, Department of Computer Sciences, The University of Texas at Austin, USA
Kagan Tumer, NASA Ames Research Lab, USA
Danny Weyns, Agentwise research group, KULeuven, Belgium
David Wolpert, NASA Ames Research Lab, USA
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Looking forward to meeting you all at LAMAS '05 and AAMAS '05.

Katja, Karl, Pieter Jan, and Sandip.

-- http://lamas2005.luc.ac.be --