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*IJCAI 2019 Workshop on Conceptual Modeling for Multi Agent
Problem Solving*
This workshop is one result of the Dagstuhl Seminar 18471 on
Next-generation Domain-Specific Modeling: Principles and Methods
<https://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=18471>
Call for Papers
<http://vienna.omilab.org/repo/files/CM-MAPS/CM-MAPS2019-CfP-v4.pdf> (PDF-Download)
One of the initial aspects of artificial intelligence studied was
problem-solving by autonomous systems. But it was quickly realized
that many problems could not be solved by a single autonomous
system, and in fact, a single system may not have a complete
understanding of the complete problem being solved. Hence, with
this as background, the area of cooperative problem solving,
distributed artificial intelligence, and multi-agent systems as a
comprehensive area have been established. The key point is that
the original ideas of the problem are decomposed into
sub-problems, agents (humans or systems) being assigned to work on
sub-problems, and the need for cooperation among agents to solve
the problem. And finally, establish a coordination framework to
ensure that cooperation happens as intended, remain.
As AI is handling complex applications, like self-driving
vehicles, to IoTs driven AI backed smart solutions, the core
issues of the problem, sub-problems, cooperation, and coordination
will surface, and become very relevant. In the implemented complex
solutions, from IBM Watson's based systems to a conversational
agent, the problem is attempted to solve made up of multiple
subproblems, and yet, the characterization of the subproblems and
their interrelationship is not explicitly represented. The
representation can be languages that glue the coordination among
deployed sub-systems or higher level artifacts that convey the
overall cooperation to solve the problem.
Conceptual modeling based on a conceptual model has constructs
with a well-defined meaning, and an ontology to convey by language
or visual diagram the concepts, their attributes, and
interrelationships among the concepts. The area of knowledge
representation also uses constructs to represent and model
knowledge for comprehension and processing. The knowledge graphs
are primitive knowledge representation techniques that do not
capture the processing and enactment aspects of the problem being
solved by a multi-agent system. Further, cooperative frameworks
driven AI-based systems provide new capabilities, discover
extended relationships among constructs and solve the problems in
a novel and creative way. Thus, new computing paradigms are wanted
to address the key issues and challenges in modeling and
development of new generation multi-agent systems.
The aim of the workshop is to bring the conceptual modelers,
requirements specifiers, multi-agent language specifiers, formal
process modelers, and cooperative problem solvers to get together
and open this area of research to help the designers and solution
providers of large AI systems to visualize, comprehend, discuss,
evolve, and enact the AI system. Our proposed ideas from the
workshop can help deploy, manage, monitor and control large AI
systems, and work towards efficient and qualitatively better
problem-solving multi-agent AI systems. These large AI systems can
be orchestrated by the execution of tasks orchestrated by events
and coordinated by a workflow management system.
The key topics of interest for the workshop are:
* Conceptual artifacts to visualize and compose multi-agent
problem
solvers and their requirements
* Languages to specify and reason about high-level problem solving
* Cooperation frameworks among multiple agents to solve a problem
* Task allocation and quality and efficiency issues
* Workflow driven coordination to enact and deploy multi-agent
problem
solver
* Formalisms to bridge conceptual and formal models for decision
and
learning multi-agent AI systems
* Evolution and change management for multi-agent problem solving
* Agent Capability modeling
* Ontology modelling and specification for agent and problem
solving
Submission
Submitted papers must be formatted according to IJCAI guidelines
(check
https://www.ijcai.org/authors_kit). All contributions
should be atmost six (6) pages, five (5) pages maximum for
content, and one (1) page for references.
Submissions should only be made electronically as PDF documents
via paper submission site:
https://easychair.org/my/conference.cgi?conf=cmmaps2019
Important Dates
* Apr 12, 2019: Deadline for submission of contributions to the
workshop
* May 10, 2019: Paper acceptance/rejection notification
* May 24, 2019: Deadline for camera-ready paper versions
* Aug 10-12, 2019: IJCAI 2019 Workshops
*Organization*
Organizing Committee:
* Kamal Karlapalem, IIIT Hyderabad, India,
kamal@iiit.ac.in
<mailto:kamal@iiit.ac.in>,
https://www.iiit.ac.in/people/faculty/kamal/
* P Radha Krishna, National Institute of Technology (NIT)
Warangal,
India
prkrishna@nitw.ac.in <mailto:prkrishna@nitw.ac.in>,
https://www.nitw.ac.in/faculty/id/16934/
* Dominik Bork, University of Vienna,
dominik.bork@univie.ac.at
<mailto:dominik.bork@univie.ac.at>,
http://homepage.dke.univie.ac.at/bork/
Program Committee (tentative):
* Robert Andrei Buchman, Babes Bolyal University Cluj Napoca,
Romania,
* Yi CAI, South China Univ of Technology
* Peter Fettke, Deutsche Forschungszentrum für Künstliche
Intelligenz,
Germany
* Aurona Gerber, University of Pretoria, South Africa
* Knut Hinkelmann, FHNW Northwestern Switzerland, Switzerland
* Julio Cesar Leite, PUC de Rio de Janeiro, Rio de Janeiro,
Brazil,
* Maurizio Lenzerini, Università di Roma, Italy
* Qing Li, Poly U, HK
* Wolfgang Maass, Saarland University, Germany
* Heinrich C. Mayr, Alpen-Adria Universität Klagenfurt, Austria
* John Mylopoulos, University of Toronto
* Praveen Paruchuri, IIIT Hyderabad, India
* David V. Pynadath, USC Institute for Creative Technologies, USA
* Kurt Sandkuhl, University of Rostock, Germany
* Hannes Schlieter, TU Dresden, Germany
* Bernhard Thalheim, Christian-Albrechts-University Kiel, Germany
* Isabelle Wattiau, ESSEC Business School, Paris, France
* Manuel Wimmer, JKU Linz, Austria
* Robert Woitsch, BOC Asset Management GmbH, Vienna, Austria
* Takahira Yamaguchi, Keio University, Japan
* Yan Liu Fiona, Poly U, HK
* Xiao-Ming Wu, Poly U, HK
* Shuai Li, Poly U, HK
--
Dr. Dominik Bork
University of Vienna
Faculty of Computer Science
Research Group Knowledge Engineering
Room: 4.19
Währinger Straße 29, 1090 Vienna
Phone: +43-1-4277-789 22
eFax: +43-1-4277-878922
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