Computational Intelligence Methodologies for Multi-Objective Optimization and Decision-Making in Autonomous Systems
In one of our recent papers, we provide an overview about the methodologies and challenges of multi-criteria decision-making algorithms for autonomous systems.
Decision- making is usually required when we are confronted with conflicting objectives and is in fact a very challenging task even for human decision-makers. This is due to the fact that we first need to find all the possible optimal alternatives and then select the right choice using a decision policy. Replacing the human decision-maker with an autonomous system in highly dynamic environments is even more challenging. We need to enable such autonomous systems to change their (pre-defined) decision policy according to unpredictable circumstances. This ability can contribute to the applicability of such autonomous systems in critical missions, such as rescue robotics where the intervention of a human-controller is not always possible. The challenge is not only in finding and selecting the best optimal alternative, but also in acting in a limited timeframe during the mission. This chapter contains an overview about the challenges and methodologies for two different time scales. Our major focus is on navigation and exploration tasks.
Sanaz Mostaghim, Computational Intelligence Methodologies for Multi-Objective Optimization and Decision-Making in Autonomous Systems, Springer, Special Edition on Women in Computational Intelligence, Alice Smith (Editor). To be published in 2021.