Search Results - "Vamplew, Peter"

Using psycholinguistic features for profiling first language of authors.

Authors: Torney, Rosemary Vamplew, Peter Yearwood, John

Source: Journal of the American Society for Information Science & Technology. Jun2012, Vol. 63 Issue 6, p1256-1269. 14p.

AI apology: a critical review of apology in AI systems.

Authors: Harland, Hadassah Dazeley, Richard Senaratne, Hashini et al.

Source: Artificial Intelligence Review; Dec2025, Vol. 58 Issue 12, p1-78, 78p

A not-so-harmless experiment in predatory open access publishing.

Authors: Martin, Alexandre Martin, Tristan

Source: Learned Publishing. Oct2016, Vol. 29 Issue 4, p301-305. 5p. 1 Color Photograph.

Subjects: Acquisition of manuscripts, Unpublished materials, Vamplew, Peter, Periodical publishing, Periodical selection

Persistent rule-based interactive reinforcement learning.

Authors: Bignold, Adam Cruz, Francisco Dazeley, Richard et al.

Source: Neural Computing & Applications; Nov2023, Vol. 35 Issue 32, p23411-23428, 18p

Human engagement providing evaluative and informative advice for interactive reinforcement learning.

Authors: Bignold, Adam Cruz, Francisco Dazeley, Richard et al.

Source: Neural Computing & Applications; Sep2023, Vol. 35 Issue 25, p18215-18230, 16p

Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario.

Authors: Cruz, Francisco Dazeley, Richard Vamplew, Peter et al.

Source: Neural Computing & Applications; Sep2023, Vol. 35 Issue 25, p18113-18130, 18p

AI apology: interactive multi-objective reinforcement learning for human-aligned AI.

Authors: Harland, Hadassah Dazeley, Richard Nakisa, Bahareh et al.

Source: Neural Computing & Applications; Aug2023, Vol. 35 Issue 23, p16917-16930, 14p

Explainable reinforcement learning for broad-XAI: a conceptual framework and survey.

Authors: Dazeley, Richard Vamplew, Peter Cruz, Francisco

Source: Neural Computing & Applications; Aug2023, Vol. 35 Issue 23, p16893-16916, 24p

Discrete-to-deep reinforcement learning methods.

Authors: Kurniawan, Budi Vamplew, Peter Papasimeon, Michael et al.

Source: Neural Computing & Applications; Feb2022, Vol. 34 Issue 3, p1713-1733, 21p

The impact of environmental stochasticity on value-based multiobjective reinforcement learning.

Authors: Vamplew, Peter Foale, Cameron Dazeley, Richard

Source: Neural Computing & Applications; Feb2022, Vol. 34 Issue 3, p1783-1799, 17p

A Prioritized objective actor-critic method for deep reinforcement learning.

Authors: Nguyen, Ngoc Duy Nguyen, Thanh Thi Vamplew, Peter et al.

Source: Neural Computing & Applications; Aug2021, Vol. 33 Issue 16, p10335-10349, 15p

Applying design science to develop a systems framework for securing digital learning: Insights from resource-constrained educational settings.

Authors: Perannagari, Krishna Teja Mythiri, Bnrajan

Source: International Journal of Technology Management & Sustainable Development. Mar2026, Vol. 25 Issue 1, p93-119. 27p.

An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments.

Authors: Ding, Kewen Vamplew, Peter Foale, Cameron et al.

Source: Knowledge Engineering Review; 2025, Vol. 40, p1-29, 29p

A taxonomy of griefer type by motivation in massively multiplayer online role-playing games.

Authors: Achterbosch, Leigh Miller, Charlynn Vamplew, Peter

Source: Behaviour & Information Technology; Aug2017, Vol. 36 Issue 8, p846-860, 15p, 3 Graphs

A novel dimensionality reduction and optimal deep learning based intrusion detection system for internet of things.

Authors: Ponniah, Krishna Kumar Retnaswamy, Bharathi

Source: Journal of Intelligent & Fuzzy Systems; 2023, Vol. 45 Issue 3, p4737-4751, 15p

A NetHack Learning Environment Language Wrapper for Autonomous Agents.

Authors: Goodger, Nikolaj Vamplew, Peter Foale, Cameron et al.

Source: Journal of Open Research Software; 2023, Vol. 11 Issue 1, p1-N.PAG, 10p

Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021)

Authors: Vamplew, Peter Smith, Benjamin J. Källström, Johan et al.

Source: Autonomous Agents & Multi-Agent Systems; Oct2022, Vol. 36 Issue 2, p1-19, 19p

A practical guide to multi-objective reinforcement learning and planning.

Authors: Hayes, Conor F. Rădulescu, Roxana Bargiacchi, Eugenio et al.

Source: Autonomous Agents & Multi-Agent Systems; Apr2022, Vol. 36 Issue 1, p1-36, 36p

Human-aligned artificial intelligence is a multiobjective problem.

Authors: Vamplew, Peter Dazeley, Richard Foale, Cameron et al.

Source: Ethics & Information Technology; Mar2018, Vol. 20 Issue 1, p27-40, 14p, 1 Graph

Assessing the impact of griefing in MMORPGs using self-determination theory.

Authors: Achterbosch, Leigh Vamplew, Peter March, Evita

Source: Computers in Human Behavior; Dec2024, Vol. 161, pN.PAG-N.PAG, 1p