Advances in Preference Handling (M-PREF) at IJCAI 2016 has been
extended by one week. The new deadline is now *May 8th*.
M-PREF is a non-archival venue and there will be no published
proceedings. However, informal proceedings will be provided at the
workshop and the papers will be posted on the website. Therefore it
will be possible to submit to other conferences and journals both in
parallel and subsequent to M-PREF 2016.
CALL FOR PAPERS:
10th Multidisciplinary Workshop on
Advances in Preference Handling (M-PREF)
New York, USA, during July 9-11, 2016
in conjunction with IJCAI-16.
Preference handling has become a flourishing topic. There are many
interesting results, good examples for cross-fertilization between
disciplines, and many new questions.
Preferences are a central concept of decision making. As preferences
are fundamental for the analysis of human choice behavior, they are
becoming of increasing importance for computational fields such as
artificial intelligence, databases, and human-computer interaction as
well as for their respective applications. Preference models are
needed in decision-support systems such as web-based recommender
systems, in automated problem solvers such as configurators, and in
autonomous systems such as Mars rovers. Nearly all areas of artificial
intelligence deal with choice situations and can thus benefit from
computational methods for handling preferences. Preference handling is
also important for machine learning as preferences may guide learning
behavior and be subject of dedicated learning methods. Moreover,
social choice methods are also of key importance in computational
domains such as multi-agent systems.
This broadened scope of preferences leads to new types of preference
models, new problems for applying preference structures, and new kinds
of benefits. Preferences are studied in many areas of artificial
intelligence such as knowledge representation & reasoning, multi-agent
systems, game theory, social choice, constraint satisfaction, decision
making, decision-theoretic planning, and beyond. Preferences are
inherently a multi-disciplinary topic, of interest to economists,
computer scientists, operations researchers, mathematicians and more.
This workshop promotes this broadened scope of preference handling and
continues a series of events on preference handling at AAAI-02,
Dagstuhl in 2004, IJCAI-05, ECAI-06, VLDB-07, AAAI-08, ADT-09,
ECAI-2010, ECAI-2012, IJCAI-13, AAAI-14, and IJCAI-15. At the
previous edition of ADT-15 and LPNMR-15, which were co-located, one of
the conclusions was that collaboration between the two areas can be
very fruitful and should be fostered.
The workshop will provide a forum for presenting advances in
preference handling and for exchanging experiences between researchers
facing similar questions, but coming from different fields. The
workshop builds on the large number of AI researchers working on
preference-related issues, but also seeks to attract researchers from
databases, multi-criteria decision making, economics, etc.
TOPICS OF INTEREST
The workshop on Advances in Preference Handling addresses all
computational aspects of preference handling. This includes methods
for the elicitation, learning, modeling, representation, aggregation,
and management of preferences and for reasoning about preferences. The
workshop studies the usage of preferences in computational tasks from
decision making, database querying, web search, personalized
human-computer interaction, personalized recommender systems,
e-commerce, multi-agent systems, game theory, social choice,
combinatorial optimization, planning and robotics, automated problem
solving, perception and natural language understanding and other
computational tasks involving choices. The workshop seeks to improve
the overall understanding of and best methodologies for preferences in
order to realize their benefits in the multiplicity of tasks for which
they are used. Another important goal is to provide
cross-fertilization between the numerous sub-fields that work with
* Preference handling in artificial intelligence
* Preference handling in database systems
* Preference handling in multiagent systems
* Applications of preferences
* Preference elicitation
* Preference representation and modeling
* Properties and semantics of preferences
* Practical preferences
*May 8th*, 2016: Workshop paper submission deadline.
May 20th, 2016: Notification on workshop paper submissions.
June 1st, 2016: Camera-ready copy due to organizers.
July 9th, 10th, or 11th, 2016: M-PREF'16 Workshop.
Researchers interested in preference handling from AI, OR, DB, CS or
other computational fields may submit a paper formatted according to
the IJCAI Formatting Instructions and up to 6 pages in length + 1 page
for references in PDF format. Workshop submissions and camera ready
versions will be handled by EasyChair. Feel free to submit either
anonymized or non-anonymized versions of your work. We have enabled
anonymous reviewing so EasyChair will not reveal the authors unless
you chose to do so in your submission.
At least one author from each accepted paper must register for the
workshop. Please see the IJCAI 2016 Website for information about
accommodation and registration.
Link to the paper submission page on EasyChair:
Markus Endres, University of Augsburg (Germany)
Nicholas Mattei, Optimization Research Group, Data 61 (NICTA) and
New South Wales (Australia)
Andreas Pfandler, TU Wien (Austria) and University of Siegen (Germany)
Thomas Allen, University of Kentucky
Stefano Bistarelli, Università di Perugia
Sylvain Bouveret, LIG - Grenoble INP
Darius Braziunas, Kobo Inc.
Jan Chomicki, University at Buffalo
Paolo Ciaccia, University of Bologna
James Delgrande, Simon Fraser University
Matthias Ehrgott, Lancaster
Gabor Erdelyi, Universitaet Siegen
Johannes Fürnkranz, TU Darmstadt
Judy Goldsmith, University of Kentucky
Souhila Kaci, LIRMM
Jérôme Lang, LAMSADE
Nicolas Maudet, Université Pierre et Marie Curie
Vincent Mousseau, LGI, Ecole Centrale Paris
Patrice Perny, LIP6
Maria Silvia Pini, University of Padova
Francesca Rossi, University of Padova and Harvard University
Scott Sanner, University of Toronto
Alexis Tsoukias, CNRS - LAMSADE
Kristen Brent Venable, Tulane University and IHMC
Paolo Viappiani, CNRS and LIP6, Univ Pierre et Marie Curie
Toby Walsh, UNSW and Data61/NICTA
Antonius Weinzierl, Vienna University of Technology
Paul Weng, SYSU-CMU JIE
Lirong Xia, RPI
Neil Yorke-Smith, American University of Beirut
Yong Zheng, DePaul University
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