Human-Agent Collaboration

A.I. and Autonomy Lab Project

Foundations of Human-Agent Collaboration:
Situation-Relevant Information Sharing

There is a pressing need for the development of new techniques in collaborative systems for representing and reasoning about joint task achievement in dynamic environments. As automated systems become more sophisticated in their capabilities, the design of effective interaction with human operators becomes more demanding (e.g., the recent Toyota Announcement).

New techniques in collaborative systems for representing
and reasoning about joint task achievement.

This project tackles a challenging problem faced in collaborative systems. Human-agent robotic teamwork, also termed human-automation teamwork, involving teams comprised of software agents, robots and humans, is increasingly being exploited to carry out tasks such as remote management of air or ground vehicles, and robot-assisted search and rescue operations. Such use of software assistants and physical robots to support human activities will increase in coming years. When action outcomes can be uncertain, successful collaborative activity cannot be fully pre-scripted, but must allow for adjustment as events unfold.

The goal of this project is to discover new algorithms and software prototypes that will support the development of human-automation teams that can coordinate and collaborate in fast changing task environments.

Research Demos

  • Tic-Tac-Toe Video

    Video of our multi-agent planner, MA-PRP, running on a Nao playing Tic-Tac-Toe against a person

  • Related Venues and Groups

    Conferences / Symposia / Workshops


    Prospective Students

    PhD applicants invited to apply for 2014/5 - the agentlab presently has a range of exciting research opportunities for graduates to do their thesis in the area of intelligent agents - funded by APA, APAI, Melbourne University scholarships and NICTA top-up scholarships and DSI (STRAPA) scholarships, please contact Adrian Pearce for more information if you are interested in applying.

    Project Information

    Funding source ARC Grant DP130102825
    Project time frame 2013 - 2015
    Contact Liz Sonenberg