Collective and Individual Decision-Making Algorithms for Autonomous Systems, Sanaz Mostaghim
Sprache des Titels:
This talk is about the recent advances in multi-objective optimization and decision-making techniques 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, since we first need to find all the possible optimal alternatives and then select the right choice using a decision policy. In this talk, we replace the human decision-maker with an autonomous system and provide novel methodologies for multi-criteria decision-making on a range of scenarios in which the autonomous systems are confronted with conflicting objectives during the mission. Enabling such systems to autonomously decide can contribute to their applicability 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 alternative, but also in acting in a limited timeframe during the mission. One more focus of the talk is on the individual vs. collective decision-making algorithms. We will show that collective learning of a decision policy can help both the individual and the collective to act in an efficient way.