Agent-Human Collaboration and Learning for Improving Human Satisfaction with Sarit Kraus
We consider environments where a set of human workers needs to handle a large set of tasks while interacting with human users. The arriving tasks vary: they may differ in their urgency, their difficulty, and the required knowledge and time duration in which to perform them. Our goal is to decrease the number of workers, which we refer to as operators that are handling the tasks while increasing the users’ satisfaction. We present automated intelligent agents that will work together with the human operators in order to improve the overall performance of such systems and increase both operators’ and users’ satisfaction. Examples include: home hospitalization environment where remote specialists will instruct and supervise treatments that are carried out at the patients' homes; operators that tele-operate autonomous vehicles when human intervention is needed, and bankers that provide online service to customers. The automated agents could support the operators: the machine learning-based agent follows the operator’s work and makes recommendations, helping him interact proficiently with the users. The agents can also learn from the operators and eventually replace the operators in many of their tasks.
Sarit Kraus is a Professor of Computer Science at Bar-Ilan University. Her research is focused on intelligent agents and multi-agent systems (including people and robots). Her application domains have included physical security, intelligent cars, human training, recommendation systems, automated negotiations and mediation, virtual humans, and rehabilitation. Kraus was awarded the IJCAI Computers and Thought Award, ACM SIGART Agents Research award, the EMET prize, and was twice the winner of the IFAAMAS influential paper award. She is a AAAI, ECCAI and ACM fellow and a recipient of the advanced ERC grant. She was named the 2020-2021 ACM Athena Lecturer for her contributions to artificial intelligence, notably to multiagent systems, human-agent interaction, autonomous agents, and nonmonotonic reasoning, in addition to exemplary service and leadership in these fields.