215 citations · 535 across the 22 of their papers we have counts for
26 papers
Diverse, Top-k, and Top-Quality Planning Over Simulators
Lyndon Benke, Tim Miller, Michael Papasimeon +1
Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classic…
Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs
Vilde B. Gjærum, Inga Strümke, Anastasios M. Lekkas +1
Although many machine learning methods, especially from the field of deep learning, have been instrumental in addressing challenges within robotic applications, we cannot take full…
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Christian Muise, Vaishak Belle, Paolo Felli +4
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, pla…
Modelling Strategic Deceptive Planning in Adversarial Multi-Agent Systems
Lyndon Benke, Michael Papasimeon, Tim Miller
Deception is virtually ubiquitous in warfare, and should be a central consideration for military operations research. However, studies of agent behaviour in simulated operations ha…
Collaborative Human-Agent Planning for Resilience
Ronal Singh, Tim Miller, Darryn Reid
Intelligent agents powered by AI planning assist people in complex scenarios, such as managing teams of semi-autonomous vehicles. However, AI planning models may be incomplete, lea…
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Ronal Singh, Upol Ehsan, Marc Cheong +2
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the…