447 citations · 905 across the 32 of their papers we have counts for
8 papers · 1 filter
DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completion
Yinghui Li, Qianyu Zhou, Jingyu Gong +4
Point Transformers (PoinTr) have shown great potential in point cloud completion recently. Nevertheless, effective domain adaptation that improves transferability toward target dom…
AI Apology: A Critical Review of Apology in AI Systems
Hadassah Harland, Richard Dazeley, Hashini Senaratne +3
Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The em…
Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI
Hadassah Harland, Richard Dazeley, Peter Vamplew +3
Emerging research in Pluralistic Artificial Intelligence (AI) alignment seeks to address how intelligent systems can be designed and deployed in accordance with diverse human needs…
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment
Peter Vamplew, Conor F Hayes, Cameron Foale +2
Reinforcement learning (RL) is a valuable tool for the creation of AI systems. However it may be problematic to adequately align RL based on scalar rewards if there are multiple co…
Data-driven Machinery Fault Diagnosis: A Comprehensive Review
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the mas…
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Peter Vamplew, Cameron Foale, Conor F. Hayes +9
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the u…
Issues with Value-Based Multi-objective Reinforcement Learning: Value Function Interference and Overestimation Sensitivity
Peter Vamplew, Ethan, Watkins +2
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives,…
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
Kewen Ding, Peter Vamplew, Cameron Foale +1
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility func…