Distributed intelligence

AI machine learning concept art

The challenge

AI has found its way to almost every aspect of our lives, from entertainment to industry and military applications. However, the scale of various application domains (e.g., patrolling large areas) requires the incorporation of multiple intelligent agents that can cooperate to deliver the desirable impact. 

Extending AI algorithms to support multi-agent and artificial swarm settings is a challenging endeavour that needs to deal with several issues including coordination, partial observability, scalability, survivability improvement and interaction with humans.

The solution

Novel methodologies are needed to address the complexity of multi-agent and swarm settings. Our team adopts multidisciplinary approaches that take inspiration from biological systems while utilising the state of the art in AI and machine learning. 

We focus on designing solutions for the following swarm and multi-agent problems: guidance and control, teaming with humans, collective decision making, multi-agent learning, transparency and knowledge representation.

The impact 

Our team’s work has been published in top-tier journals and conferences and has attracted a number of research grants. We’ve been supervising several undergraduate and HDR students, designing and teaching undergraduate courses, and organising tutorials and special sessions at prestigious conferences. Examples of our impact include:

Related people and partners

Level B - Research Focused Aya Hussein
Level B - Research Focused
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Lecturer Heba El-Fiqi
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Senior Lecturer Tim Lynar
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Computing and Cyber Security Program Coordinator Erandi Hene Kankanamge
Computing and Cyber Security Program Coordinator
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