Learning Dynamic Systems For Intention Recognition In Human Robot Cooperation

This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.

This book gathers revised and extended versions of the best papers presented at the 8th International Joint Conference on Computational Intelligence (IJCCI 2016), which was held in Porto, Portugal from 9 to 11 November 2016. The papers address three main fields of Computational Intelligence, namely: Evolutionary Computation, Fuzzy Computation, and Neural Computation. In addition to highlighting recent advances in these areas, the book offers veteran researchers new and innovative solutions, while also providing a source of information and inspiration for newcomers to the field.