Distilling Relevant Information to Support Human-Human and Human-Agent Collaboration (Ofra Amir, DSI Learning Club)

Ofra Amir (Faculty, Technion – Israel Institute of Technology).

Distilling relevant information to support human-human and human-agent collaboration

Abstract:

One of today’s biggest challenges is the heightened complexity and information overload stemming from increasingly interacting systems, consisting of both humans and machines. In this talk, I will describe work that aims to address this challenge in two settings: human teamwork and human-agent collaboration. In the context of human teamwork, I will present our work towards developing intelligent systems that reduce coordination overhead in distributed teams by personalizing the information that is shared with team members. We developed an algorithm that determines what information about others’ activities is most relevant to each team member, and show through a user study that such personalized information sharing resulted in higher productivity and reduced workload of team members, without detrimental effects on the quality of the team’s work.

In the context of human-agent collaboration, I will describe our work towards the development of methods for summarizing agent behavior, with the goal of enabling users to better understand the capabilities of agents they interact with. We developed “HIGHLIHGTS’”, an algorithm that extracts important trajectories from the execution trace of an agent to generate a succinct description of key agent behaviors. Our experiments show that study participants were more successful at assessing agents’ capabilities when shown summaries generated by HIGHLIGHTS compared to baseline summaries.