This is the home page of the third symposium on:
ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS
AAMAS-3
Venue and Contact:
  • AISB'03 Convention, CS Dept., University of Wales, Aberystwyth
  • About the Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB)
  • AAMAS contact: info@aamas.net
Preliminary Programme:
Symposium Chair:
  • Dimitar Kazakov, CS Dept., University of York, Heslington, York YO10 5DD, UK
  • E-mail: info@aamas.net
  • For contacts until 12 Dec 2002
    Department of Intelligent Systems, Jozef Stefan Institute

  • Jamova 39, 1000 Ljubljana, Slovenia
    tel. +386 1 477 3693, fax: +386 1 4251 038.
Co-Chairs:
Programme Committee:
  • 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.
  • Michael Schroeder, Department of Computing, City University, London. 
  • Kostas Stathis, Department of Computing, City University, London. 
  • Eugenio Oliveira, Department of Computing and Electrical Engineering, University of Porto.
  • Enric Plaza, IIIA-CSIC, Spain.
  • Keynote Speaker:
    Call for Papers
      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 ML experts and agent system experts, 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 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.

    14. Electronic submissions of extended abstracts of up to four pages should be submitted as postscript or PDF files 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 E-mail rather than the paper itself.

    Important Dates:
    • New Submission Deadline for Extended Abstracts: 20 January 2003
    • Notification re: Extended Abstracts:17 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)
    Past symposia:
    Related Publications:
    • Proc. of AAMAS
    • Proc. of AAMAS-2
    • Book on AAMAS (forthcoming)
    Related Projects:

    Maintained by D. Kazakov. Last update: 18 Feb 2003. Contact: info@aamas.net