120 citations · 172 across the 12 of their papers we have counts for
6 papers · 1 filter
Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning
Andrés Holgado-Sánchez, Peter Vamplew, Richard Dazeley +2
Value-aware AI should recognise human values and adapt to the value systems (value-based preferences) of different users. This requires operationalization of values, which can be p…
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios
Francisco Cruz, Adam Bignold, Hung Son Nguyen +2
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning rese…
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey
Richard Dazeley, Peter Vamplew, Francisco Cruz
Broad Explainable Artificial Intelligence moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machin…
Levels of explainable artificial intelligence for human-aligned conversational explanations
Richard Dazeley, Peter Vamplew, Cameron Foale +3
Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers fo…
Persistent Rule-based Interactive Reinforcement Learning
Adam Bignold, Francisco Cruz, Richard Dazeley +2
Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-t…
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment
Ithan Moreira, Javier Rivas, Francisco Cruz +3
Robots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to in…