Scientific Direction

The aim of this research theme is to investigate and engineer massive cooperation, which we call "extreme cooperation", that is, to create general models, find insights, discover fundamental principles and design algorithms to enable a collection or system of (tens, hundreds, thousands or even millions/trillions of) autonomous entities (things/systems/individuals) spread out over a geographical area, each with its/his/her own agenda, to cooperate (mainly with their nearby neighbours but possibly beyond, using the Internet or other forms of communications) in such a way that their respective goals/agenda are fulfilled (at least in part) yet yielding globally positive impact (measured in terms of individual welfare or the system as a whole) and fulfilling global goals (e.g., overall law and order with societal rules obeyed, ethical behaviour maintained, trusted interactions, secure and privacy preferences of individuals preserved, etc). The expanding Internet of Things has made this topic interesting.

There is a wide range of phenomena within this theme, for example:

• a collection of cars trying to get from A to B but coordinating to reduce traffic congestion
• a collection of cars trying to find car park can cooperate to help each other park sooner
• a collection of automated cars attempting to drop off passengers at the same building
• a collection of drones trying to serve a given population in an area
• a large crowd of people trying to move as quickly as possible through an area or evacuating a building
• a city saturated with a large number of robots or robotic (smart) things interacting and automating city functions
• a large crowd of people being contributors to a crowdsourcing system for mapping certain phenomena (bandwidth map, crowd map, parking maps, etc)
• a collection of separately owned devices (e.g., smartphones and on-body sensors) attempting to sense a group-based phenomena or collective behaviours
• a crowd of separately owned devices (e.g., smartphones) working together in an ad hoc manner to compute a global result or search an area
• a large collection of Internet connected smart things working together to solve an issue or debug a problem
• a very large ad hoc supply chain with numerous independent entities involved and coordinating towards overall goals
• a collection of smart things cooperate with each other and humans to provide comprehensive personalised aged care

Inspiration and models can be drawn from a wide range of sources, including distributed computing, complex systems science (e.g., swarms), multiagent systems and AI, the social sciences, such as game-theoretic computational economics, and biology, such as biologically inspired self-organising systems.

Applications could include decentralised coordination of urban mobility, distributed governance in smart cities, enacting wide-scale social change, and streamlining businesses.

Contact: seng.loke@deakin.edu.au