215 citations · 535 across the 22 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi, Ana Marasović, Tim Miller +1
Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely,…
Distal Explanations for Model-free Explainable Reinforcement Learning
Prashan Madumal, Tim Miller, Liz Sonenberg +1
In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions. Ou…
What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS
Guang Hu, Tim Miller, Nir Lipovetzky
Epistemic planning --- planning with knowledge and belief --- is essential in many multi-agent and human-agent interaction domains. Most state-of-the-art epistemic planners solve t…
A Grounded Interaction Protocol for Explainable Artificial Intelligence
Prashan Madumal, Tim Miller, Liz Sonenberg +1
Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans. Succ…
Contrastive Explanation: A Structural-Model Approach
Tim Miller
This paper presents a model of contrastive explanation using structural casual models. The topic of causal explanation in artificial intelligence has gathered interest in recent ye…
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
Prashan Madumal, Tim Miller, Frank Vetere +1
To generate trust with their users, Explainable Artificial Intelligence (XAI) systems need to include an explanation model that can communicate the internal decisions, behaviours a…
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences
Tim Miller, Piers Howe, Liz Sonenberg
In his seminal book `The Inmates are Running the Asylum: Why High-Tech Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams Indianapolis, IN, USA], Alan Cooper argues…
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of th…