As multi-agent systems become ubiquitous, the ability to satisfy multiple system-level constraints in these systems grows increasingly important. In applications ranging from automated cruise control to safety in robot swarms, barrier functions have emerged as a tool to provably meet such constraints by guaranteeing forward invariance of a set. However, satisfying multiple constraints typically implies formulating multiple barrier functions, bringing up the need to address the degree to which multiple barrier functions may be composed through Boolean logic.
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 Tue, 09/19/2017 - 10:39am
IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC 2018)
IEEE ISORC was founded in 1998 (with its first meeting in Kyoto, Japan) to address research into the application of real-time object-oriented distributed technology. Since then, ISORC has continually evolved to meet the latest challenges faced by researchers and practitioners in the real-time domain, with an emphasis on object-, component- and service- oriented systems and solutions..
