7th Multidisciplinary Workshop on

Advances in Preference Handling


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, 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 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 recommender systems

Preference handling in multiagent systems

Game theory

(Combinatorial) auctions and exchanges

Social choice, 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

Personalized recommendation systems

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