215 citations · 535 across the 22 of their papers we have counts for
9 papers · 1 filter
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…
Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions
Henrietta Lyons, Eduardo Velloso, Tim Miller
As the use of algorithmic systems in high-stakes decision-making increases, the ability to contest algorithmic decisions is being recognised as an important safeguard for individua…
Fair and Responsible AI: A Focus on the Ability to Contest
Henrietta Lyons, Eduardo Velloso, Tim Miller
As the use of artificial intelligence (AI) in high-stakes decision-making increases, the ability to contest such decisions is being recognised in AI ethics guidelines as an importa…
Designing for Contestation: Insights from Administrative Law
Henrietta Lyons, Eduardo Velloso, Tim Miller
A paper presented at the Workshop on Contestability in Algorithmic Systems at CSCW 2019. Challenging algorithmic decisions is important to decision subjects, yet numerous factors c…
Deceptive Reinforcement Learning for Privacy-Preserving Planning
Zhengshang Liu, Yue Yang, Tim Miller +1
In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour p…
Directive Explanations for Actionable Explainability in Machine Learning Applications
Ronal Singh, Paul Dourish, Piers Howe +4
This paper investigates the prospects of using directive explanations to assist people in achieving recourse of machine learning decisions. Directive explanations list which specif…