kazakov@cs.york.ac.uk
Date: Mon 25 Nov 2002 - 11:41:32 GMT
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Apologies for multiple posting
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Call for Papers of the Third Symposium on
ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS (AAMAS-3)
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(http://www-users.cs.york.ac.uk/~kazakov/aamas/aamas-3.html)
Organised as part of the Artificial Intelligence
and Simulation of Behaviour (AISB) 2003 Convention
(http://aisb.aber.ac.uk)
University of Wales, Aberystwyth, 7-11 April 2003
Motivation:
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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.
When designing agent systems, it is impossible to foresee all the
potential situations an agent may encounter and specify the agents'
behaviour optimally in advance. Agents therefore have to learn from
and adapt to their environment. This task is even more complex when
the agent is situated in an environment that contains other agents
with potentially different capabilities and goals. Multi-Agent
Learning, i.e., the ability of the agents to learn how to co-operate
and compete, becomes crucial in such domains.
The goal of this symposium is to increase awareness and interest in
adaptive agent research, encourage collaboration between experts in
ML, agent systems, and other related fields, and give a representative
overview of current research in the area of adaptive agents. The
symposium will serve as an inclusive forum for the discussion on
ongoing or completed work in both theoretical and practical issues.
The symposium is a continuation of AAMAS, held as part of AISB-01 in
York, March 2001, and AAMAS-2, held in London as part of the AISB-02
in April 2002. The AAMAS symposia (http://www.aamas.net/) are the
first scientific meetings on adaptive and learning agents in the UK,
and the success in the previous two years has clearly confirmed the
need for such forum.
The symposium topic is situated at the intersection of two areas,
namely, Adaptation/Learning and Agents, which would naturally relate
to the general convention theme: Cognition in Machines and
Animals. The symposium will focus on (but is not necessarily limited
to) the following topics:
1.Adaptive Mobile Agents: adaptation of and to platforms (adaptive
body rather than mind).
2.From Single Agent to Multi-Agent Learning: The ability to learn is
especially important for an agent when there are other agents acting
in the environment. An important open question is whether and how
single-agent learning techniques can be modified and applied in a
multi-agent setting.
3.Learning of Co-ordination: It is obvious that agents in a
multi-agent system need to co-ordinate their action, whether in a
co-operative or competitive manner. It is often not feasible to
develop a good co-ordination protocol from scratch, and therefore
agents need to acquire co-ordination skills by
learning. Co-ordination (and learning of it) can be studied under
several different assumptions, e.g., with/without communication or
with/without mutual observation, depending on the application area
and the respective restrictions.
4.Learning and Communication: When several learning agents work in a
team it may be beneficial for them to cooperate not just on the task
achievement but also on the learning process itself. Clearly,
communication is an important tool for such co-operation.
5.Distributed Learning: The major question in this area is how agents
can learn in a collaborative way as a group. This is in contrast to
the alternative view on multi-agent learning where agents in a group
learn individually and separate theories are obtained.
6.Evolutionary Agents: Natural selection can be employed to evolve in
a generation of agents with inherited properties the phenotype that
best fits the agents' goals in a given environment. The approach
has been successfully applied to social simulation and other
multi-agent domains.
7.Emergent Organisation/Behaviour and Studies of Complexity in
Multi-Agent Systems with Learning and Adaptation: Understanding of
how properties such as functional organisation, adaptability and
robustness can emerge from complex learning systems. Also, studies
of the computational complexity of learning algorithms and its
impact on the way in which learning and recall are balanced in MAS.
8.Evolution of Individual Learning in Multi-Agent Systems: Individual
agents can use personal experience in order to improve their
performance throughout their lifespan. Alternatively, reproduction
and natural selection can be employed in a MAS to evolve agents that
are best suited for the task. The two approaches can be combined,
and natural selection used to evolve agents with an optimal learning
bias. This topic also links natural selection, language and
learning through the evolutionary search for the best language (or
language bias) used for learning.
9.Game-Theoretical and Analytical Approaches to Adaptive Multi-Agent
Systems: No research on learning agents will be complete if it does
not draw on the body of work in Game Theory and Systems Theory in
order to compare its results with these two areas and provide a
unifying view of the different aspects of interaction within a
system, resp. among agents, that each approach provides.
10.Logic-Based Learning: The ability to incorporate background
knowledge into the agents' decision-making and learning processes
is arguably essential for effective performance in complex, dynamic
domains. Logic-based learning mechanisms such as explanation-based
learning and inductive logic programming are commonly used in such
situations. The MAS setup brings in several specific issues, such
as reasoning about time and in time (for an action to be taken),
the ability to communicate observations and theories, and to cope
with inaccurate or misleading information.
11.Learning in Reactive Agents: Learning in a setup where the only
model of the world an agent has is the world itself. (Also:
learning of control, sub-symbolic and lazy learning).
12.Learning for Real-Time Applications: An agent typically performs a
number of tasks, learning being just one of them. Time complexity
of learning and validity of results, trade-off between learning
(improving performance) and recall (using acquired knowledge to
perform other, non-learning, tasks) are all relevant to this
topic. Flexible Real-Time Systems (RTS) are a hot research topic,
yet very little has been done to combine results from ML and RTS
areas.
13.Industrial and Large Scale Applications of Learning Agents: Agent
technology is already having a strong impact on various
applications, including e-commerce, entertainment, human-computer
interfaces, and plant control. Many of these applications are being
equipped with machine learning technology.
Electronic submissions of extended abstracts of up to four pages
should be submitted as a postscript, PDF or MS Word document to
info@aamas.net with the subject line "paper submission" by 6 Jan 2003.
Up to five keywords of the author's choice should be listed in the
email rather than the paper itself.
Important Dates:
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Extended abstract submission deadline: 6 January 2003
Notification about extended abstracts: 10 February 2003
Submission of Full Papers: 7 March 2003
Convention: 7 - 11 April 2003 (the exact days and duration of
the symposium are yet to be set)
Symposium Chair:
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Dimitar Kazakov, CS Dept., University of York,
Heslington, York, YO10 5DD UK
Postal address until 12 Dec 2002:
Department of Intelligent Systems,
Jozef Stefan Institute Jamova 39,
1000 Ljubljana, Slovenia
tel. +386 1 477 3693 (until 12 Dec 2002)
fax: +386 1 4251 038 (until 12 Dec 2002)
Email: info@aamas.net
Co-Chairs:
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Daniel Kudenko, CS Dept., University of York
Eduardo Alonso, Dept. of Computing, City University
Programme Committee:
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Frances Brazier, Department of AI, Free University, Amsterdam.
Niek Wijngaards, Department of AI, Free University, Amsterdam.
Ann Nowé, Free University of Brussels.
Kurt Driessens, Computer Science Department, Catholic University of Leuven.
Tom Holvoet, Computer Science Department, Catholic University of Leuven.
Saso Dzeroski, Jozef Stefan Institute, Ljubljana.
Philippe De Wilde, Imperial College, London.
Kostas Stathis, Department of Computing, City University, London.
Eugenio Oliveira, Dept. of Computing and Electrical Engineering, Univ. of
Porto.
Enric Plaza, IIIA-CSIC, Spain.
Keynote Speaker:
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Sorin Solomon (Racah Institute of Physics, The Hebrew University of
Jerusalem)
Web page: http://shum.cc.huji.ac.il/~sorin/
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