In many real-world applications, machine learning algorithms are employed as a tool in a constructive process. These processes are similar to the general knowledge-discovery process but have a more specific goal: the construction of one-or-more domain elements with particular properties. The most common use of machine learning algorithms in this context is to predict the properties of candidate domain elements.

In this workshop we want to bring together domain experts employing machine learning tools in constructive processes and machine learners investigating novel approaches or theories concerning constructive processes as a whole. The concerned machine learning approaches are typically interactive (e.g., online- or active-learning algorithms) and have to deal with huge, relational in- and/or output spaces.

Interesting applications include but are not limited to: de novo drug design, generation of art (e.g., music composition), construction of game levels, generation of novel food recipes, proposal of travel itineraries, etc. Interesting approaches include but are not limited to: active approaches to structured output learning, transfer or multi-task learning of generative models, active search or online optimisation over relational domains, and learning with constraints.

Many of the applications of constructive machine learning, including the ones mentioned above, are primarily considered in their respective application domain research area but are hardly present at machine learning conferences. By bringing together domain experts and machine learners working on constructive ML, we hope to bridge this gap between the communities.