Human-centered AI requires that AI systems are able to adapt to humans, to understand the preferences underlying human choice behavior, and to take them into account when interacting with humans or when acting on their behalf. Preference models are needed in decision-support systems such as web-based recommender systems, in digital assistants and chatbots, 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 while gaining new capabilities such as explainability and revisability of choices. Preference handling is also important for machine learning as preferences may guide learning behaviour and be subject of dedicated learning methods. Moreover, social choice methods are of key importance in computational domains such as multi-agent systems. Preferences are studied in many areas of artificial intelligence such as knowledge representation & reasoning, multi-agent systems, game theory, computational social choice, constraint satisfaction, logic programming and non-monotonic reasoning, decision making, decision-theoretic planning, and beyond. Preferences are inherently a multi-disciplinary topic, of interest to economists, computer scientists (including AI, databases, and human-computer interaction), operations researchers, mathematicians and more.

This broad set of application areas leads to new types of preference models, new problems for applying preference structures, and new kinds of benefits. The workshop on Advances in Preference Handling studies these questions and addresses all computational aspects of preference handling. This includes methods for the elicitation, learning, modeling, representation, aggregation, and management of preferences as well as methods 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, computational social choice, combinatorial optimization, automated problem solving, non-monotonic reasoning, planning and robotics, perception and natural language understanding, and other computational tasks involving choices. A particular challenge consists in using preferences for explaining decisions and for counterfactual reasoning based on hypothetical preference change. Another challenge is to explore new application areas for preferences such as sustainable development and digital healthcare systems. 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 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 and learning

• Preference representation and modeling

• Properties and semantics of preferences

• Practical preferences