ILP 2016 - 26th International Conference on Inductive Logic Programming

Inductive Logic Programming is a subfield of machine learning, which uses logic programming as a uniform representation technique for examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining. The ILP conference series, started in 1991, is the premier international forum for learning from structured relational data. Originally focusing on the induction of logic programs, over the years it has expanded its research horizon significantly and welcomes contributions to all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches.
We are pleased to announce that the 26th International Conference on Inductive Logic Programming (ILP 2016) will be held in London from Sunday 4th to Tuesday 6th of September 2016. It will be held at the Warren House Conference Centre, situated next to Richmond Park (UK Nature Reserve and the largest London Royal Park) and well connected to the centre of London via tubes and trains.
Typical, but not exclusive, topics of interest for submissions include:
Theoretical aspects: logical-foundations of learning; computational/statistical learning theory; specialisation and generalisation; probabilistic logic-based learning; graph and tree mining.
Representation and languages for learning: logic programming; Datalog; first-order logic; description logic and ontologies; higher-order logic; Answer Set Programming; probabilistic logic languages; constraint logic programming; knowledge graphs.
Algorithms and systems: learning with (semi-)structured data; (semi-)supervised and unsupervised relational learning; relational reinforcement learning; predicate invention; propositionalisation approaches; multi-instance learning; learning in the presence of uncertainty; meta-level learning.
Applications of learning: art; bioinformatics; systems biology; games; medical informatics; robotics; natural language processing; web-mining; software engineering; modelling and adaptation of control systems; socio-technical systems.