The focus is on recognizing and analyzing the challenges that arise when autonomous agents with different capabilities need to interact and collaborate on unknown tasks, on providing methods for the automated design of these environments to promote collaboration, and on specifying guarantees regarding the quality of the design solutions produced by our suggested methods. This research combines data-driven approaches with symbolic AI techniques and involves both theoretical work and evaluations on multi-agent reinforcement learning settings and on multi robot systems.
Promoting multi-agent collaboration via dynamic markets of information and skills in which AI agents and robots trade their physical capabilities and their ability to acquire new information. The value of these traded commodities is dynamically computed based on the agents’ objectives, sensors and actuation capabilities as well as their ability to communicate with each other and ask for assistance. This framework maximizes performance and team resilience, without relying on a centralized controller.
Most current approaches to robotic planning separate the low-level planning of basic behaviors and the high-level search for a sequence of behaviors that will accomplish a task. However, in complex settings such as packing, personal assistance, and cooking, this dichotomous view becomes inefficient, especially in environments shared by multiple autonomous agents. We therefore offer new ways for integrating task-level considerations when planning the robot’s movement, and for propagating motion-planning considerations into task planning.
