default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDEnda Howley
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j30]Abdul Jabbar Saeed Tipu, Padraig Ó Conbhuí, Enda Howley:
Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system. Clust. Comput. 27(1): 71-90 (2024)- [j29]Xue Yang
, Enda Howley, Michael Schukat:
ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning. Comput. Secur. 141: 103825 (2024) - [j28]Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Radulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin:
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. Expert Syst. Appl. 249: 123686 (2024) - [c33]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. AAMAS 2024: 2717-2721 - [i11]Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel de Oliveira Ramos, Roxana Radulescu, Willem Röpke, Diederik M. Roijers:
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. CoRR abs/2402.02665 (2024) - 2023
- [j27]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning. Auton. Agents Multi Agent Syst. 37(2): 26 (2023) - [j26]Abdul Jabbar Saeed Tipu, Padraig Ó Conbhuí, Enda Howley:
Seismic data IO and sorting optimization in HPC through ANNs prediction based auto-tuning for ExSeisDat. Neural Comput. Appl. 35(8): 5855-5888 (2023) - [j25]Nicola Mc Donnell, Jim Duggan, Enda Howley:
A Genetic Programming-based Framework for Semi-automated Multi-agent Systems Engineering. ACM Trans. Auton. Adapt. Syst. 18(2): 6:1-6:30 (2023) - [c32]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - [c31]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. IJCAI 2023: 5711-5719 - [i10]Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers:
Distributional Multi-Objective Decision Making. CoRR abs/2305.05560 (2023) - [i9]Xue Yang, Enda Howley, Michael Schukat:
ADT: Agent-based Dynamic Thresholding for Anomaly Detection. CoRR abs/2312.01488 (2023) - 2022
- [j24]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022) - [j23]Abdul Jabbar Saeed Tipu, Padraig Ó Conbhuí, Enda Howley:
Applying neural networks to predict HPC-I/O bandwidth over seismic data on lustre file system for ExSeisDat. Clust. Comput. 25(4): 2661-2682 (2022) - [j22]Rachael Shaw, Enda Howley, Enda Barrett:
Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers. Inf. Syst. 107: 101722 (2022) - [c30]Conor F. Hayes, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Decision-Theoretic Planning for the Expected Scalarised Returns. AAMAS 2022: 1621-1623 - [i8]Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Radulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin:
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. CoRR abs/2204.05027 (2022) - [i7]Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models. CoRR abs/2207.00368 (2022) - [i6]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning. CoRR abs/2211.13032 (2022) - 2021
- [c29]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning. AAMAS 2021: 1530-1532 - [i5]Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search. CoRR abs/2102.00966 (2021) - [i4]Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i3]Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion:
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making. CoRR abs/2106.01048 (2021) - 2020
- [j21]Kevin McDonnell, Nicholas Waters, Enda Howley, Florence Abram:
Chordomics: a visualization tool for linking function to phylogeny in microbiomes. Bioinform. 36(4): 1309-1310 (2020) - [j20]Nicola Mc Donnell, Enda Howley, Jim Duggan:
Dynamic virtual machine consolidation using a multi-agent system to optimise energy efficiency in cloud computing. Future Gener. Comput. Syst. 108: 288-301 (2020) - [j19]Rachael Shaw, Enda Howley, Enda Barrett:
An intelligent ensemble learning approach for energy efficient and interference aware dynamic virtual machine consolidation. Simul. Model. Pract. Theory 102: 101992 (2020) - [c28]Nicola Mc Donnell, Enda Howley, Jim Duggan:
Evolved Gossip Contracts - A Framework for Designing Multi-agent Systems. PPSN (1) 2020: 637-649
2010 – 2019
- 2019
- [j18]Rachael Shaw, Enda Howley, Enda Barrett:
An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simul. Model. Pract. Theory 93: 322-342 (2019) - [j17]Martin Duggan, Rachael Shaw, Jim Duggan, Enda Howley, Enda Barrett:
A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers. Softw. Pract. Exp. 49(4): 617-639 (2019) - [c27]Rachael Shaw, Enda Howley, Enda Barrett:
An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification. ICSOC 2019: 251-266 - 2018
- [j16]Karl Mason, Jim Duggan, Enda Howley:
A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning. Appl. Soft Comput. 62: 148-161 (2018) - [j15]Karl Mason, Martin Duggan, Enda Barrett, Jim Duggan, Enda Howley:
Predicting host CPU utilization in the cloud using evolutionary neural networks. Future Gener. Comput. Syst. 86: 162-173 (2018) - [j14]Patrick Mannion, Sam Devlin, Jim Duggan, Enda Howley:
Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning. Knowl. Eng. Rev. 33: e23 (2018) - [c26]Rachael Shaw, Enda Howley, Enda Barrett:
A Predictive Anti-Correlated Virtual Machine Placement Algorithm for Green Cloud Computing. UCC 2018: 267-276 - [i2]Seyed Sajad Mousavi, Michael Schukat, Enda Howley:
Deep Reinforcement Learning: An Overview. CoRR abs/1806.08894 (2018) - 2017
- [j13]Martin Duggan, Jim Duggan, Enda Howley, Enda Barrett:
A network aware approach for the scheduling of virtual machine migration during peak loads. Clust. Comput. 20(3): 2083-2094 (2017) - [j12]Patrick Mannion, Sam Devlin, Karl Mason, Jim Duggan, Enda Howley:
Policy invariance under reward transformations for multi-objective reinforcement learning. Neurocomputing 263: 60-73 (2017) - [j11]Karl Mason, Jim Duggan, Enda Howley:
Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants. Neurocomputing 270: 188-197 (2017) - [j10]Patrick Mannion, Sam Devlin, Jim Duggan, Enda Howley:
Multi-agent credit assignment in stochastic resource management games. Knowl. Eng. Rev. 32: e16 (2017) - [j9]Martin Duggan, Jim Duggan, Enda Howley, Enda Barrett:
A reinforcement learning approach for the scheduling of live migration from under utilised hosts. Memetic Comput. 9(4): 283-293 (2017) - [c25]Patrick Mannion, Jim Duggan, Enda Howley:
A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games. AAMAS 2017: 1625-1627 - [c24]Karl Mason, Jim Duggan, Enda Howley:
Neural network topology and weight optimization through neuro differential evolution. GECCO (Companion) 2017: 213-214 - [c23]Karl Mason, Jim Duggan, Enda Howley:
Evolving multi-objective neural networks using differential evolution for dynamic economic emission dispatch. GECCO (Companion) 2017: 1287-1294 - [c22]Rachael Shaw, Enda Howley, Enda Barrett:
An advanced reinforcement learning approach for energy-aware virtual machine consolidation in cloud data centers. ICITST 2017: 61-66 - [c21]Martin Duggan, Karl Mason, Jim Duggan, Enda Howley, Enda Barrett:
Predicting host CPU utilization in cloud computing using recurrent neural networks. ICITST 2017: 67-72 - [c20]Rachael Shaw, Enda Howley, Enda Barrett:
Predicting the Available Bandwidth on Intra Cloud Network Links for Deadline Constrained Workflow Scheduling in Public Clouds. ICSOC 2017: 221-228 - [i1]Seyed Sajad Mousavi, Michael Schukat, Peter Corcoran, Enda Howley:
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning. CoRR abs/1704.08883 (2017) - 2016
- [j8]Peter Vrancx, Enda Howley, Matt Knudson:
Preface to the special issue: adaptive learning agents. Knowl. Eng. Rev. 31(1): 1-2 (2016) - [c19]Patrick Mannion, Karl Mason, Sam Devlin, Jim Duggan, Enda Howley:
Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract). AAMAS 2016: 1345-1346 - [c18]Martina Curran, Enda Howley, Jim Duggan:
An Analytics Framework to Support Surge Capacity Planning for Emerging Epidemics. Digital Health 2016: 151-155 - [c17]Martin Duggan, Jim Duggan, Enda Howley, Enda Barrett:
An Autonomous Network Aware VM Migration Strategy in Cloud Data Centres. ICCAC 2016: 24-32 - [c16]Seyed Sajad Mousavi, Michael Schukat, Enda Howley:
Deep Reinforcement Learning: An Overview. IntelliSys (2) 2016: 426-440 - [p1]Patrick Mannion, Jim Duggan, Enda Howley:
An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control. Autonomic Road Transport Support Systems 2016: 47-66 - 2015
- [c15]Patrick Mannion, Jim Duggan, Enda Howley:
Parallel Reinforcement Learning for Traffic Signal Control. ANT/SEIT 2015: 956-961 - [c14]Karl Mason, Enda Howley:
Avoidance Strategies in Particle Swarm Optimisation. MENDEL 2015: 3-15 - 2014
- [j7]Sam Devlin, Daniel Hennes, Enda Howley:
Preface to the special issue: Adaptive Learning Agents, Part 1. Connect. Sci. 26(1): 5-6 (2014) - [j6]Enda Barrett, Jim Duggan, Enda Howley:
A parallel framework for Bayesian reinforcement learning. Connect. Sci. 26(1): 7-23 (2014) - [j5]Sam Devlin, Daniel Hennes, Enda Howley:
Preface to the special issue: Adaptive Learning Agents Part 2. Connect. Sci. 26(2): 101-102 (2014) - [c13]Ian Broderick, Enda Howley:
Particle Swarm Optimisation with Enhanced Memory Particles. ANTS Conference 2014: 254-261 - 2013
- [j4]Enda Barrett, Enda Howley, Jim Duggan:
Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurr. Comput. Pract. Exp. 25(12): 1656-1674 (2013) - 2011
- [j3]Enda Howley, Jim Duggan:
Investing in the Commons: a Study of Openness and the Emergence of Cooperation. Adv. Complex Syst. 14(2): 229-250 (2011) - [j2]Declan Mungovan, Enda Howley, Jim Duggan:
The influence of random interactions and decision heuristics on norm evolution in social networks. Comput. Math. Organ. Theory 17(2): 152-178 (2011) - [c12]Enda Howley, Jim Duggan:
Tag-based cooperation in N-player dilemmas. AAMAS 2011: 1211-1212 - [c11]Enda Barrett, Enda Howley, Jim Duggan:
A Learning Architecture for Scheduling Workflow Applications in the Cloud. ECOWS 2011: 83-90 - [c10]Hongliang Liu, Enda Howley, Jim Duggan:
Particle swarm optimisation with gradually increasing directed neighbourhoods. GECCO 2011: 29-36 - 2010
- [c9]Hongliang Liu, Enda Howley, Jim Duggan:
The Impact of Market Preferences on the Evolution of Market Price and Product Quality. MALLOW 2010
2000 – 2009
- 2009
- [c8]Declan Mungovan, Enda Howley, Jim Duggan:
Norm Convergence in Populations of Dynamically Interacting Agents. AICS 2009: 219-230 - [c7]Enda Howley, Jim Duggan:
The Effects of Evolved Sociability in a Commons Dilemma. ALA 2009: 33-48 - [c6]Hongliang Liu, Enda Howley, Jim Duggan:
Optimisation of the Beer Distribution Game with complex customer demand patterns. IEEE Congress on Evolutionary Computation 2009: 2638-2645 - [c5]Enda Barrett, Enda Howley, Jim Duggan:
Evolving Group Coordination in an N-Player Game. ECAL (1) 2009: 450-457 - 2008
- [c4]Enda Howley, Colm O'Riordan:
The Effects of Payoff Preferences on Agent Tolerance. ALIFE 2008: 249-256 - 2007
- [c3]Enda Howley, Colm O'Riordan:
Agent Interactions and Implicit Trust in IPD Environments. Adaptive Agents and Multi-Agents Systems 2007: 87-101 - 2006
- [j1]Enda Howley, Colm O'Riordan:
The effects of viscosity in choice and refusal IPD environments. Artif. Intell. Rev. 26(1-2): 103-114 (2006) - [c2]Enda Howley, Colm O'Riordan:
The Effects and Evolution of Implicit Trust in Populations Playing the Iterated Prisoner's Dilemma. IEEE Congress on Evolutionary Computation 2006: 793-799 - 2005
- [c1]Enda Howley, Colm O'Riordan:
The emergence of cooperation among agents using simple fixed bias tagging. Congress on Evolutionary Computation 2005: 1011-1016
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-21 00:09 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
