Call for Papers Second Symposium on Adaptive Agents and Multi-Agent Systems (AAMAS-2), AISB'02 Convention, April 2002, Imperial College, London.
Motivation In recent years, Intelligent agents and multi-agent systems have become a highly active area of AI research. Intelligent Agents have been developed and applied successfully in many domains, such as e-commerce, human-computer interaction, entertainment, process management and traffic control. When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behavior optimally in advance. Agents therefore have to learn from and adapt to their environment. This task is even more complex when nature is not the only source of uncertainty, and the agent is situated in an environment that contains other agents with potentially different capabilities, goals, and beliefs. Multi-Agent Learning, i.e., the ability of the agents to learn how to cooperate 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 proposed symposium is a continuation of the Symposium on Adaptive Agents and Multi-Agent Systems, held as part of AISB-01 in York, March 2001. The event was a pioneering experience, as no symposium on learning agents had been organised previously in the UK. The success of the symposium has encouraged us to propose AAMAS-2. Chair: Eduardo Alonso Department of Computing City University Northampton Square, London EC1V 0HB United Kingdom [EMAIL PROTECTED] Co- Chair: Daniel Kudenko, Department of Computer Science, University of York Co- Chair: Dimitar Kazakov, Department of Computer Science, University of York Programme Committee: - Eugenio Oliveira, Department of Computing and Electrical Engineering, University of Porto. - Pete Edwards, Department of Computer Science, University of Aberdeen. - Niek Wijngaards, Department of Artificial Intelligence, Vrije Universiteit, Amsterdam. - Michael Schroeder, Department of Computing, City University. - Kostas Stathis, Department of Computing, City University. - Kurt Driessens, Computer Science Department, Catholic University of Leuven. Keynote Speaker Luc Steels, from Free University of Brussels, will give a keynote talk at the symposium. Topics of Interest The proposed symposium will focus on (but is not limited to) the following areas: 1. Learning and adaptation in Multi-Agent Systems: 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 adapted to and applied in a multi-agent setting. 2. Logic-based learning: The ability to incorporate background knowledge to 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 being used to test this hypothesis. 3. 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 cooperation. 4. Natural selection, language and learning: These three issues are inter-linked through the evolutionary search for the best language bias used for learning. 5. Evolutionary agents and emergent Multi-Agent structures: Genetic algorithms are a particular machine learning approach that has been successfully applied to social simulation and other multi-agent domains. Specific techniques are still under development. One focus of this research area is on observing emergent behaviors. 6. Industrial 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. 7. 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. Submissions Initially, we require an extended abstract, up to four pages in length (at least 10pt font). The following formats are acceptable: - Paper: A4, 3 copies - Email: PDF, Postscript, or MS Word Please submit your abstracts on or before 21st December 2001. Please post or email submissions to the programme chair (address given above). Full papers (submitted after the extended abstract has been accepted) should be no longer than 12 pages. Accepted symposium papers will be published by AISB and the proceedings will have an ISBN number. Timetable Abstract submission deadline 21st December 2001 Notification re: extended abstracts 31st January 2002 Submission of full papers 11th March 2002 Convention 2nd - 5th April 2002 Please note, the submission of full papers deadline must not be broken because the convention starts very soon after this.
