Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
This paper is about a novel rule-based approach for reasoning about qualitative spatiotemporal relations among technology-rich autonomous objects, to which we refer to as artifact...
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Generality or refinement relations between different theories have important applications to generalization in inductive logic programming, refinement of ontologies, and coordin...
Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis--examples include social networks, Web gra...