6 citations · 8 across the 2 of their papers we have counts for
8 papers
An Experimentation Platform for Explainable Coalition Situational Understanding
Katie Barrett-Powell, Jack Furby, Liam Hiley +8
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML)…
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
Dave Braines, Federico Cerutti, Marc Roig Vilamala +3
Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the…
Explainable AI for Intelligence Augmentation in Multi-Domain Operations
Alun Preece, Dave Braines, Federico Cerutti +1
Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of r…
Learning Features of Network Structures Using Graphlets
Kun Tu, Jian Li, Don Towsley +2
Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic a…
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation
Alun Preece, Rob Ashelford, Harry Armstrong +1
Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, i…
Stakeholders in Explainable AI
Alun Preece, Dan Harborne, Dave Braines +2
There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general c…