Title : Benefits of Social Learning in Evolutionary Robotics
| Presenter | Jacqueline Heinerman |
| Abstract | Social learning in Evolutionary Robotics enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning counterparts. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this talk, I show that even within one system, the observed advantages of social learning can vary between parameter settings. These results serve as a reminder that tuning of the parameters should not be left as an afterthought because they can drastically impact the conclusions on the advantages of social learning. Additionally, the results show that lower quality parameter settings benefit more from social learning. This suggests that social learning reduces the sensitivity of the learning process to the choice of parameters. |
Title : Zooming in on Ontologies: Minimal Modules and Best Excerpts
| Presenter | Jieying Chen |
| Abstract | Knowledge about a complex system represented in ontologies yields a collection of axioms that are too large for human users to browse, let alone to comprehend or reason about it. To this end, we propose a computational framework to zoom in on large ontologies by providing users with either the necessary axioms that act as explanations for sets of entailments, or fix-sized sub-ontologies containing the most relevant information over a vocabulary. First we introduce a new notion, subsumption justification as an extension of justification (a minimal set of axioms needed to preserve a logical consequence) to capture the subsumption knowledge between a term and all other terms in the vocabulary. We present algorithms for computing subsumption justifications based on a simulation notion developed for the problem of deciding the logical difference between ontologies. We show how subsumption justifications can be used to obtain minimal modules and to compute best excerpts by additionally employing a partial Max-SAT solver. This yields two state-of-the-art methods for computing all minimal modules and all best excerpts, which we evaluate over large biomedical ontologies. |