447 citations · 697 across the 26 of their papers we have counts for
13 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…
Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety
Catalin Mitelut, Ben Smith, Peter Vamplew
The rapid advancement of artificial intelligence (AI) systems suggests that artificial general intelligence (AGI) systems may soon arrive. Many researchers are concerned that AIs a…
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…
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
Francisco Cruz, Charlotte Young, Richard Dazeley +1
Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in re…
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)
Peter Vamplew, Benjamin J. Smith, Johan Kallstrom +9
The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and…
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…
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcemen…
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…
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
Adam Bignold, Francisco Cruz, Richard Dazeley +2
Interactive reinforcement learning proposes the use of externally-sourced information in order to speed up the learning process. When interacting with a learner agent, humans may p…
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
Adam Bignold, Francisco Cruz, Matthew E. Taylor +4
A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to mo…
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario
Francisco Cruz, Richard Dazeley, Peter Vamplew +1
Robotic systems are more present in our society everyday. In human-robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order t…
A Survey of Multi-Objective Sequential Decision-Making
Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson +1
Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which…