This paper proposes an event-triggered interactive gradient descent method for solving multi-objective optimization problems. We consider scenarios where a human decision maker works with a robot in a supervisory manner in order to find the best Pareto solution to an optimization problem. The human has a time-invariant function that represents the value she gives to the different outcomes. However, this function is implicit, meaning that the human does not know it in closed form, but can respond to queries about it.
Submitted by Anonymous on Thu, 10/05/2017 - 3:27pm
Dear colleagues,
For our 5-year research project (ERATO MMSD, Metamathematics for Systems Design) we are looking for senior researchers and postdocs (10+ positions in total and several are still open), together with research assistants (PhD students) and internship students.
This broad project aims to extend the realm of formal methods from software to cyber-physical systems (CPS), with particular emphases on logical/categorical metatheories and industrial application esp. in automotive industry. The project covers diverse areas that include:
Submitted by Anonymous on Thu, 10/05/2017 - 3:27pm
Dear colleagues,
For our 5-year research project (ERATO MMSD, Metamathematics for Systems Design) we are looking for senior researchers and postdocs (10+ positions in total and several are still open), together with research assistants (PhD students) and internship students.
This broad project aims to extend the realm of formal methods from software to cyber-physical systems (CPS), with particular emphases on logical/categorical metatheories and industrial application esp. in automotive industry. The project covers diverse areas that include:
