Subject: CFPP: ECAI96 Workshop "Learning in DAI Systems"
From: Gerhard Weiss <[email-masked]>
To: [email-masked], [email-masked], [email-masked]
Date: Mon, 18 Dec 1995 15:59:43 +0100 (MET)

Dear Colleagues,

the following ECAI96 Workshop "Call for Papers and Participation" 
might be of interest to you. Please forward the CFPP to researchers
that might be also interested. (The CFPP already has been distributed
to mailing lists and newsgroups.)

Best wishes, Gerhard Weiss.

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    C a l l    f o r    P a p e r s    a n d    P a r t i c i p a t i o n 

                                 Workshop on 

            LEARNING IN DISTRIBUTED ARTIFICIAL INTELLIGENCE SYSTEMS 

                             held as part of the

           12th European Conference on Artificial Intelligence (ECAI)
                    (August 12-16, 1996, Budapest, Hungary)

                              and organized by

              Michael Huhns, University of South Carolina, USA
                   Toru Ishida, Kyoto University, Japan
               Gerhard Kraetschmar, Universitaet Ulm, Germany
              Victor Lesser,  University of Massachusetts, USA
             Jeffrey Rosenschein, The Hebrew University, Israel
   Gheorghe Tecuci, George Mason University, USA, & Romanian Academy, Romania
          Walter Van de Velde, Vrije Universiteit Brussel, Belgium
      Gerhard Weiss (Chair), Technische Universitaet Muenchen, Germany

   Workshop Description
   --------------------

   Distributed Artificial Intelligence (DAI) is concerned with the study 
   and design of systems consisting of several interacting entities which are
   logically and often spatially distributed and in some sense can be called
   autonomous and intelligent. Two primary types of such systems, or of DAI 
   systems for short, can be distinguished:  distributed problem solving and 
   planning systems, where the emphasis is on task decomposition and solution 
   synthesis, and multi-agent systems, where the emphasis is on behavior 
   coordination. Many DAI systems of both types have been described in the 
   literature, differing from each other in the entities involved (e.g., with 
   respect to their number, the number of goals pursuit by them, their degree 
   of autonomy, and their perceptual, cognitive and effectual skills) as well 
   as in the interactions between the entities (e.g., with respect to 
   frequency, level and purpose). Examples of DAI systems are collections of 
   communicating expert systems and teams of cooperating assembly robots. 
   DAI systems receive considerable attention for two major reasons. First, 
   they offer useful properties such as parallelism, robustness, and
   scalability, and therefore are applicable in many domains which cannot be 
   handled by centralized AI systems; in particular, they are well suited for 
   domains which require, e.g., the resolution of interest and goal conflicts, 
   the integration of multiple sources of knowledge or activity, the time-
   bounded processing of very large data sets, or the on-line interpretation of
   data arising in different geographical locations. And second, they are in 
   accordance with the insight gained in disciplines like AI, psychology and 
   sociology that intelligence is deeply and inevitably coupled with 
   interaction.

   DAI systems typically are very complex and hard to specify in their
   dynamics. It is therefore commonly agreed that they should be equipped 
   with the ability to learn, that is, to automatically improve their future 
   performance. Unfortunately, in contrast to this agreement and apart from a 
   few exceptions, the area of learning in DAI systems has been neglected for
   a long time. This situation has considerably changed in the past few years,
   and today this area receives steadily increasing attention within both the 
   DAI and the Machine Learning communities. The goal of the workshop is to 
   bring together researchers and practitioners being interested in this area,
   and to serve as a discussion forum that allows to evaluate the 
   state-of-the-art in DAI learning, to attack the many open questions and 
   unsolved problems, and to identify important and promising research and 
   application directions. Papers are invited that describe practical and/or 
   theoretical work on learning in DAI systems. The topics of interest include,
   but are not limited to:
 
   -  Concepts, models and architectures of learning in DAI systems.
   -  Requirements for and principles of learning in DAI systems.
   -  Algorithms and tools for learning in DAI systems.
   -  Benefits of learning DAI systems over non-learning DAI systems.
   -  Applicability and limitations of traditional machine learning 
      approaches in the context of DAI systems.
   -  Relationships between learning and cooperation, learning and
      communication, learning and self-organization, etc.
   -  Learning in DAI systems by knowledge acquisition/discovery/refinement/
      integration, by advice taking, by negotiation, by observation, etc.
   -  Parallel and distributed learning in DAI systems (e.g., inductive
      learning, reinforcement learning, etc.)
   -  Multistrategy learning in DAI systems.
   -  Organizational learning and emergent organizational behavior.
   -  Learning in robot teams.
   -  Formal models of learning in DAI systems, theory of team learning.
   -  Distributed integrated simulations with learning agents.
   -  Applications of learning DAI systems.
   -  Interdisciplinary approaches to learning in DAI systems from 
      fields like game theory, organizational theory, economics,
      psychology, sociology, etc.

   We would like to stress that not only papers presenting completed work, 
   but also papers on novel ideas and of exploratory nature are welcome. 
   We also would like to stress the interdisciplinary nature of this workshop,
   and we are keen to particularly encourage related contributions from other
   fields than AI and DAI.

   Workshop Participation and Format
   ---------------------------------

   Attendance will be by invitation only and is limited to approximately 35 
   participants. Those interested in attending the workshop are required to 
   submit either (i) an extended abstract describing their work or (ii) a 
   statement of interest. (Please note that due to the limited workshop
   size preference will be given to authors of submitted abstracts.)

   The workshop will last a full day (08/12/96 or 08/13/96), immediately prior
   to the start of the main conference. Participants will be selected by the 
   workshop organizers after reviewing all submissions. If more papers are 
   accepted than can be presented, a small poster session will be included. 
   There will be ample time alloted for discussion. Printed working notes 
   containing copies of the accepted papers will be available at the workshop.

   Submission Information
   ----------------------

   Extended abstracts:

   . Those who wish to present a paper should (i) submit a version of their 
     extended abstract (postscript, 5-7 pages, 12pt font) via anonymous ftp 
     to flop.informatik.tu-muenchen.de/pub/ws-ecai96-sub by 03/17/96, and 
     (ii) send an email notification to [email-masked]. 
   . The extended abstracts and the email notifications should include 
     (i) the authors' complete contact information and (ii) a note indicating
     the authors' preferred presentation mode (oral or poster).
   . Authors will be advised as to the acceptance status of their paper
     around 04/21/96.
   . Authors of accepted abstracts should electronically send their final 
     workshop versions (postscript, up to 12 pages, 12pt font) via anonymous 
     ftp to flop.informatik.tu-muenchen.de/pub/ws-ecai96-sub by 05/20/96. 
   
   Statements of interest:

   . Those who wish to attend the workshop without presenting a paper should 
     send a statement of interest (plain ASCII text, 1-2 pages) via email to 
     [email-masked] by 03/17/96. 
   . The statement should include (i) the author's complete contact 
     information and (ii) a description of her/his research and application 
     interests.
   . Authors will be advised as to their participation status around 04/21/96.

   Publication
   -----------

   The final versions of the accepted papers will be published in the ECAI
   working notes of the workshop. 
   Additionally, authors of accepted papers describing high-quality work will 
   be invited for publication in a special issue on "Learning in Distributed 
   Artificial Intelligence Systems" of the Journal of Experimental and 
   Theoretical Artificial Intelligence (JETAI, for journal information see
   http://turing.pacss.binghamton.edu/jetai/index.html).

   Summary of Important Dates
   --------------------------
   
   03/17/96        extended abstracts, statements of interest
   04/21/96        notification of acceptance/rejection
   05/20/96        final versions for working notes
   08/12or08/13/96 workshop

   Contact Address
   ---------------

   Questions regarding this workshop can be directed to

   Gerhard Weiss
   Institut fuer Informatik
   Technische Universitaet Muenchen
   D-80290 Muenchen, Germany
   EMAIL: [email-masked] 
   TEL:   +49 89 2105 2407
   FAX:   +49 89 2105 8207

   Registration
   ------------

   All workshop participants are expected to register for the main
   ECAI conference. A fee of ECU 50 will be charged for each workshop
   participant in addition to the normal ECAI conference registration
   fee. (Approximate exchange rates, as of October 1995: 1 ECU equals 
   1.2US$, 6.4FF, and 1.8DM.)

   WWW Page
   --------
   Information about this workshop is also available via WWW at URL:
   http://www7.informatik.tu-muenchen.de/~weissg/ws-ecai96

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