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CALL FOR PAPERS

The 16th International Conference on Modeling Decisions for Artificial Intelligence
MDAI 2019, September 4 - 6, 2019, Milan, Italy

http://www.mdai.cat/mdai2019

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In MDAI we are particularly interested in the different facets of decision processes
in a broad sense. This includes model building and all kind of mathematical tools
for data aggregation, information fusion, and decision making; tools to help decision
in data science problems (including e.g., statistical and machine learning algorithms
as well as data visualization tools); and algorithms for data privacy and
transparency-aware methods so that data processing processes and decisions made from
them are fair, transparent and avoid unnecessary disclosure of sensitive information.

The MDAI conference includes tracks on the topics of:

(i) data science
(ii) data privacy
(iii) aggregation functions
(iv) human decision making
(v) graphs and (social) networks
(vi) recommendation and search

MDAI 2019 is the 16th MDAI conference. Previous conferences were celebrated in
Barcelona (2004), Tsukuba (2005), Tarragona (2006), Kitakyushu (2007), Sabadell
(2008), Awaji Island (2009), Perpiny (2010), Changsha (2011), Girona (2012),
Barcelona (2013), Tokyo (2014), Skovde (2015), St Julia de Loria (2016),
Kitakyushu (2017), Mallorca (2018).

MDAI is rated as a CORE B conference by the Computing Research and Education
Association of Australasia - CORE.

***Important Dates***

LNAI Submission deadline: March 7th, 2019
LNAI Acceptance notification: May,2nd, 2019
Final version of LNAI accepted papers: May 22nd, 2019
Early registration: May 22nd, 2019
Conference: September 4 - 6, 2019

***Submission and Publication***

Original technical contributions are sought. Contributions will be selected on
the basis of their quality. Papers should not exceed 12 pages in total (using
LNCS/LNAI style). Proceedings with accepted papers will be
published in the LNAI/LNCS series (Springer-Verlag).

We will also publish additional proceedings in a USB memory with a later deadline.

***Tracks***

- DS track. Data science is the science of data. Its goal is to explain processes
and objects through the available data. The explanation is expected to be objective
and suitable to make predictions. The ultimate goal of the explanations is to make
informed decisions based on the knowledge extracted from the data. Original
contributions on methods, models, and tools for data science are sought.

- DP Track. Data privacy track. Privacy-preserving data mining, privacy enhancing
technologies, and statistical disclosure control provide tools to avoid disclosure,
and/or have a good balance between disclosure risk and data utility and security.
Original contributions on aspects related to data privacy are sought.

- AGOP Track. Aggregation functions. Functions to aggregate data appear in several
contexts. They are used for decision making and information fusion. Data science and
artificial intelligence systems need these functions to summarize information,
improve data quality and help in decision processes. Original contributions on
aggregation functions and their applications are sought.

- DM Track. Human decision making. Decision making is a pervasive problem in
intelligent systems, and decisions are to be made in scenarios where uncertainty is
common. Most mathematical models for decision making under risk and uncertainty provide
optimal decisions under certain constraints. Experience and studies show that these
rational decision making models diverge from the typical approach human use to make decisions.

- GSN Track. Graphs and (social) networks track. Graphs are often a convenient way
to represent data. Social networks is a paradigmatic case. Algorithms and functions to
process graphs and to extract information and knowledge from them are of high relevance
in data science. Original contributions on graph analysis are sought.

- RS Track. Recommendation and search track. Searching and recommending online
information/items to users deals with both the subjectivity related to the user's needs
and the uncertainty and vagueness that characterize the retrieval process, in particular
on the Web and on social media where huge amounts of new contents are generated every day.
For these reasons, original contributions on search and recommendation algorithms
and applications are sought.

***MDAI 2019 Organization***

General Chairs:
Gabriella Pasi (University of Milano-Bicocca)
Marco Viviani (University of Milano-Bicocca)

Program Co-chairs:
Vicenc Torra (Maynooth University, Ireland)
Yasuo Narukawa (Toho Gakuen, Japan)

Advisory Board:
D. Dubois, L. Godo, K. Hirota, J. Kacprzyk, S. Miyamoto, M. Sugeno, R. R. Yager

Organization Chair:
Fabio Stella (University of Milano-Bicocca)

***PC and additional information***

- http://www.mdai.cat/mdai2019