Learning of Relative Spatial Concepts from ambiguous instructions

Learning spatial concepts from RatSLAM representations

RatSLAM is a biologically-inspired visual SLAM and navigation system that has been shown to be effective indoors and outdoors on real robots. The spatial representations at the core of RatSLAM, the pose cells, form in a distributed fashion as the robot learns the environment. The activity in the pose cells, while being coherent, does not possess strong geometric properties, making it difficult ...

Learning from Ambiguous Examples

Learning From Ambiguous Examples

Current inductive learning systems are not well suited to learning from ambiguous examples. We say that an example is ambiguous if it has multiple interpretations, only one of which may be valid. Some domains in which ambiguous learning problems can be found are natural language processing (NLP) and computer vision. An example of an ambiguous training instance with two interpretations is shown ...

Interactive Learning from Unlabeled Instructions

Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibration procedure allows to map signals to specific meanings. In this paper,...

Concept Learning from (Very) Ambiguous Examples

We investigate here concept learning from incomplete examples, denoted here as ambiguous. We start from the learning from interpretations setting introduced by L. De Raedt and then follow the informal ideas presented by H. Hirsh to extend the Version space paradigm to incomplete data: a hypothesis has to be compatible with all pieces of information provided regarding the examples. We propose an...