In our research, we consider transfer learning scenarios where a target learner does not have access to the source data, but instead to hypotheses or models induced from it. This is called the Hypothesis Transfer Learning (HTL) problem. Previous approaches concentrated on transferring source hypotheses as a whole. We introduce a novel method for selectively transferring elements from previous h...

As any other problem solving task that employs search, AI Planning needs heuristics to efficiently guide the problem-space exploration. Machine learning (ML) provides several techniques for automatically acquiring those heuristics. Usually, a planner solves a problem, and a ML technique generates knowledge from the search episode in terms of complete plans (macro-operators or cases), or heurist...

We study how to automatically select and adapt multiple abstractions or representations of the world to support model-based reinforcement learning. We address the challenges of transfer learning in heterogeneous environments with varying tasks. We present an efficient, online framework that, through a sequence of tasks, learns a set of relevant representations to be used in future tasks. Withou...