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.
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.
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, 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, and AAAI-14. Since 2008, this series of workshops is organized
by the multidisciplinary working group on Advances in Preference Handling,
which is affiliated to the Association of European Operational Research
Societies EURO.

The workshop provides 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.


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TOPICS OF INTEREST
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The workshop on Advances in Preferences 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, e-commerce, multi-agent systems,
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 the benefits of preferences for those
tasks. Another important goal is to provide cross-fertilization
between different fields.


Preference handling in artificial intelligence
* Qualitative decision theory
* Non-monotonic reasoning
* Preferences in logic programming
* Preferences for soft constraints in constraint satisfaction
* Preferences for search and optimization
* Preferences for AI planning
* Preferences reasoning about action and causality
* Preference logic

Preference handling in database systems
* Preference query languages for SQL and XML
* Algebraic and cost-based optimization of preference queries
* Top-k algorithms and cost models
* Ranking relational data and rank-aware query processing
* Skyline query evaluation
* Preference management and repositories
* Personalized search engines

Preference handling in multiagent systems
* Game theory
* (Combinatorial) auctions and exchanges
* Voting, and other rating/ranking systems
* Mechanism design and incentive compatibility

Applications of preferences
* Web search
* Decision making
* Combinatorial optimization and other problem solving tasks
* Personalized human-computer interaction
* e-commerce and m-commerce

Preference elicitation
* Preference elicitation in multi-agent systems
* Preference elicitation with incentive-compatibility
* Learning of preferences
* User preference mining
* Revision of preferences

Preference representation and modeling
* Linear and non-linear utility representations
* Multiple criteria/attributes
* Qualitative decision theory
* Graphical models
* Logical representations
* Soft constraints
* Relations between qualitative and quantitative approaches

Properties and semantics of preferences
* Preference and choice
* Preference composition, merging, and aggregation
* Incomplete or inconsistent preferences
* Intransitive indifference
* Reasoning about preferences

Comparison of approaches, cross-fertilization, interdisciplinary work


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WORKSHOP CHAIRS
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Darius Braziunas, Kobo Inc, Toronto, Canada
Markus Endres, University of Augsburg, Germany
K. Brent Venable, Tulane University and IHMC, USA
Paul Weng, SYSU-CMU Joint Institute of Engineering, China
Lirong Xia, Rensselaer Polytechnic Institute, Troy, NY, USA