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Today's Topics:
1. FINAL CFP: 10th Multidisciplinary Workshop on Advances in
Preference Handling (M-PREF) 2016 with IJCAI 2016 (Nicholas Mattei)
2. One Ph.D. position in Approximation Algorithms at IDSIA,
University of Lugano, Switzerland (Fabrizio Grandoni)
3. Open Post Doc Position in Operations research at INRIA Lille
, INOCS team (Luce Brotcorne)
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Message: 1
Date: Tue, 19 Apr 2016 16:48:21 +1000
From: Nicholas Mattei <nsmattei@gmail.co
To: dmanet@zpr.uni-koeln.de
Subject: [DMANET] FINAL CFP: 10th Multidisciplinary Workshop on
Advances in Preference Handling (M-PREF) 2016 with IJCAI 2016
Message-ID:
<CAKa3QmDerGW6Gaqp_hq+2XCdYB5rKAbGwvc0AK+gUQ7UZCrLvw@mail.gmail.com>
Content-Type: text/plain; charset=UTF-8
======================================================================
Final CALL FOR PAPERS:
10th Multidisciplinary Workshop on
Advances in Preference Handling (M-PREF)
New York, USA, July 9, 2016
in conjunction with IJCAI-16.
http://www.mpref-2016.preflib.org
======================================================================
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
preferences.
* 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
======================================================================
IMPORTANT DATES
======================================================================
May 1st, 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.
=====================================================================
SUBMISSION
=====================================================================
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:
https://easychair.org/conferences/?conf=mpref16
======================================================================
WORKSHOP CHAIRS
======================================================================
Markus Endres, University of Augsburg (Germany)
Nicholas Mattei, Optimization Research Group, Data 61 (NICTA) and
University of
New South Wales (Australia)
Andreas Pfandler, TU Wien (Austria) and University of Siegen (Germany)
======================================================================
PROGRAM COMMITTEE
======================================================================
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
Ulrich Junker
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
--
*Nicholas Mattei*
Senior Researcher | Optimisation / Algorithmic Decision Theory
Lecturer | University of New South Wales (UNSW)
*DATA61 | CSIRO*
E nicholas.mattei@nicta.com.au T +61 2 8306 0464 W www.nickmattei.net
Neville Roach Laboratory (UNSW Campus), Locked Bag 6016, Sydney NSW 1466,
Australia
CSIRO's Digital Productivity business unit and NICTA have joined forces to
create digital powerhouse Data61
------------------------------
Message: 2
Date: Tue, 19 Apr 2016 13:19:19 +0200
From: Fabrizio Grandoni <fabrizio.grandoni@gmail.com>
To: dmanet@zpr.uni-koeln.de
Subject: [DMANET] One Ph.D. position in Approximation Algorithms at
IDSIA, University of Lugano, Switzerland
Message-ID:
<CAJv5pPBj6ptf=nOcO7RcL4dbVSD7Wxu36HY8KGKPu5H_VgpzbA@mail.gmail.com>
Content-Type: text/plain; charset=UTF-8
The Algorithms and Complexity Group of IDSIA, University of Lugano
(Switzerland), opens one Ph.D. position in the area of approximation
algorithms. The position is supported by the SNSF Grant "Approximation
Algorithms for Network Problems", that already supports another Ph.D.
student and one PostDoc.
The position is for 3 years, with the possibility of an extension by 1
year (subject to approval by SNSF). The gross salary is roughly 50.000
CHF per year (low taxes). There is generous travel support.
The ideal candidate should be bright and creative, and hold (or be
close to obtaining) a Master Degree in Computer Science or related
areas. A solid background in Algorithms, Computational Complexity,
Discrete Mathematics, Probability Theory, and/or Graph Theory is
helpful. A good knowledge of written and spoken English is also
required (while knowledge of Italian is not needed).
Team members will have the opportunity to cooperate with the
Algorithms and Complexity group at IDSIA, which currently consists of
8 researchers. IDSIA offers an international working environment.
Lugano is a tidy and lively town, with a wonderful view on Ceresio
lake and mountains around. Ticino Canton offers many opportunities for
hiking, biking, skiing, etc. Restaurants serve very good (Italian
style!) food.
Any interested candidate should email a detailed CV (including a list
of passed Master degree exams with marks, a list of publications, if
any, and up to 3 references) to "fabrizio@idsia.ch" as soon as
possible. The position will be filled as soon as a strong candidate
will apply. In order to receive full consideration, please email your
CV by the end of April.
Further details about the project, the position, and how to apply can
be found at:
http://people.idsia.ch/~grandoni/SNF2015.html
For any question, do not hesitate to contact:
Prof. Fabrizio Grandoni
fabrizio@idsia.ch
http://people.idsia.ch/~grandoni/
------------------------------
Message: 3
Date: Tue, 19 Apr 2016 16:15:24 +0200 (CEST)
From: Luce Brotcorne <luce.brotcorne@inria.fr>
To: DMANET@zpr.uni-koeln.de
Subject: [DMANET] Open Post Doc Position in Operations research at
INRIA Lille , INOCS team
Message-ID:
<1217478036.25249043.1461075324147.JavaMail.zimbra@inria.fr>
Content-Type: text/plain; charset=ISO-8859-1
A Post-Doc position in Operations research is available at INRIA Lille (France) in the INOCS team :
https://team.inria.fr/inocs/
Title of the proposal : New Models and Methods for Demand Response in a Smart Grid Context
Supervisor of the Post-doc : Luce Brotcorne, Miguel F. Anjos (INRIA International Chair), Martine Labbé.
Keywords : Smart Grid, Optimization, Bilevel problems.
Do not hesitate to send your cv and contact Luce Brotcorne : luce.brotcorne@inria.fr before april 21 2016.
Job offer description
Context:
Electricity is a critical source of energy for our society. Due to the fundamental importance of electricity, economic growth is inevitably accompanied by a corresponding growth in the demand for electricity. However, investment in the power system infrastructure almost invariably lags this growth in demand, resulting in a reduction (and sometimes near absence) of spare supply, and hence a tightly constrained operating context. The operating time period of highest power consumption are called peaks and represent a major concern for the system operator.
A smart grid is the combination of a traditional electrical power production, transmission and distribution system with two-flows of information and of energy between suppliers and consumers. This combination is expected to deliver energy savings, cost reductions, and increased reliability and security. Nevertheless it induces new challenges to operate the resulting system. These include using the power grid already in place more efficiently, integrating renewable energy sources such as wind and solar power generation, managing the flows of power and of information, and integrating loads as active participants in the grid operations.
A central challenge in the full implementation of the smart grid is the effective integration of the customers as active participants in the grid, a process generally referred to as demand response . To capture this non cooperative sequential decision making process, we use a leader-follower approach. In its simplest form, this game-theoretic framework includes two players, namely the leader and the follower. The leader intrinsically integrates the reaction of the follower in its optimization process. The resulting model is a bilevel optimization problem.
Goal:
The goal of the project is i) to develop new bilevel optimization models to represent demand response in a smart grid energy management context, ii) to design and test new algorithms to solve the problem.
More precisely the objectives consists in extending the models propose by Afsar et al. [1] by taking into account the fact that the customers may decide to adjust their consumption or decline to consume and by integrating renewable energy sources. The algorithms developed to solve the problem (exact methods, or matheuristics) will be sharply based on the structure of the problem.
Skills and profile
Good knowledge in mathematical programming, combinatorial optimization, algorithmic.
Ability in programming in C++ or java. Knowledge of optimization solvers such as CPLEX or GUROBI is an add.
------------------------------
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End of dmanet Digest, Vol 98, Issue 19
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