News

  • 24 June: Best paper is announced in the awards.
  • 8 May 2023: Camera-ready copies of all papers are viewable in the program.
  • 4 May 2023: Stay tuned for the camera-ready PDF's of each paper, which will be available on the website shortly.
  • 3 May 2023: The program for the workshop is now available.
  • 3 May 2023: We are excited to announce Shimon Whiteson as a keynote speaker for ALA 2023.
  • 25 April 2023: We are excited to announce Peter Stone as a keynote speaker for ALA 2023.
  • 21 April 2023: We are excited to announce Christopher Amato as a keynote speaker for ALA 2023.
  • 17 April 2023: Information regarding the Neural Computing & Applications Journal Special Issue and Best Paper Award are now posted.
  • 31 March 2023: The list of accepted papers is now online.
  • 24 March 2023: Stay tuned! ALA 2023 acceptances will be announced on March 27th.
  • 24 Jan 2023: ALA 2023 submission deadline has been extended to 24 Feb 2023 23:59 UTC
  • 22 Dec 2022: ALA 2023 Submission Link can be found here
  • 22 Dec 2022: ALA Call for Papers can be found here
  • 22 Dec 2022: ALA 2023 Website goes live!

ALA 2023 - Workshop at AAMAS 2023

Adaptive and Learning Agents (ALA) encompasses diverse fields such as Computer Science, Software Engineering, Biology, as well as Cognitive and Social Sciences. The ALA workshop will focus on agents and multiagent systems which employ learning or adaptation.

This workshop is a continuation of the long running AAMAS series of workshops on adaptive agents, now in its fifteenth year. Previous editions of this workshop may be found at the following urls:

The goal of this workshop is to increase awareness of and interest in adaptive agent research, encourage collaboration and give a representative overview of current research in the area of adaptive and learning agents and multi-agent systems. It aims at bringing together not only scientists from different areas of computer science (e.g. agent architectures, reinforcement learning, evolutionary algorithms) but also from different fields studying similar concepts (e.g. game theory, bio-inspired control, mechanism design).

The workshop will serve as an inclusive forum for the discussion of ongoing or completed work covering both theoretical and practical aspects of adaptive and learning agents and multi-agent systems.

This workshop will focus on all aspects of adaptive and learning agents and multi-agent systems with a particular amphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems. The topics of interest include but are not limited to:

  • Novel combinations of reinforcement and supervised learning approaches
  • Integrated learning approaches that work with other agent reasoning modules like negotiation, trust models, coordination, etc.
  • Supervised multi-agent learning
  • Reinforcement learning (single- and multi-agent)
  • Novel deep learning approaches for adaptive single- and multi-agent systems
  • Multi-objective optimisation in single- and multi-agent systems
  • Planning (single- and multi-agent)
  • Reasoning (single- and multi-agent)
  • Distributed learning
  • Adaptation and learning in dynamic environments
  • Evolution of agents in complex environments
  • Co-evolution of agents in a multi-agent setting
  • Cooperative exploration and learning to cooperate and collaborate
  • Learning trust and reputation
  • Communication restrictions and their impact on multi-agent coordination
  • Design of reward structure and fitness measures for coordination
  • Scaling learning techniques to large systems of learning and adaptive agents
  • Emergent behaviour in adaptive multi-agent systems
  • Game theoretical analysis of adaptive multi-agent systems
  • Neuro-control in multi-agent systems
  • Bio-inspired multi-agent systems
  • Applications of adaptive and learning agents and multi-agent systems to real world complex systems

Extended and revised versions of papers presented at the workshop will be eligible for inclusion in a journal special issue (see below).

Important Dates

  • Submission Deadline: 30 January 2023   24 February 2023 23:59 UTC
  • Notification of acceptance: 27 March 2023
  • Camera-ready copies: 14 April 2023
  • Workshop: 29 - 30 May 2023

Submission Details

Papers can be submitted through EasyChair.

We invite submission of original work, up to 8 pages in length (excluding references) in the ACM proceedings format (i.e. following the AAMAS formatting instructions). This includes work that has been accepted as a poster/extended abstract at AAMAS 2023. Keeping with previous ALA guidelines, papers are limited to 8 pages plus references - no supplementary material is accepted. Additionally, we welcome submission of preliminary results, i.e. work-in-progress, as well as visionary outlook papers that lay out directions for future research in a specific area, both up to 6 pages in length, although shorter papers are very much welcome, and will not be judged differently. Finally, we also accept recently published journal papers in the form of a 2 page abstract.

Furthermore, for submissions that were rejected or accepted as extended abstracts at AAMAS, we encourage authors to also append the received reviews. This is simply a recommendation and it is optional. Authors can also include a short note or changelist they carried out on the paper. The reviews can be appended at the end of the submission file and do not count towards the page limit.

All submissions will be peer-reviewed (single-blind). Accepted work will be allocated time for poster and possibly oral presentation during the workshop. Extended versions of original papers presented at the workshop will also be eligible for inclusion in a post-proceedings journal special issue.

When preparing your submission for ALA 2023, please be sure to remove the AAMAS copyright block, citation information and running headers. Please replace the AAMAS copyright block in the main.tex file from the AAMAS template with the following: 

\setcopyright{none}
\acmConference[ALA '23]{Proc.\@ of the Adaptive and Learning Agents Workshop (ALA 2023)}
{May 9-10, 2023}{Online, \url{https://ala2023.github.io/}}{Cruz, Hayes, Wang, Yates (eds.)}
\copyrightyear{2023}
\acmYear{2023}
\acmDOI{}
\acmPrice{}
\acmISBN{}
\settopmatter{printacmref=false}

Journal Special Issue

We are delighted to announce that extended versions of all original contributions at ALA 2023 will be eligible for inclusion in a special issue of the Springer journal Neural Computing and Applications (Impact Factor 5.130). The deadline for submitting extended papers will be 15 November 2023.

NCA

For further information please contact the workshop organizers and Patrick Mannion.

Program

All times are presented in local London time.

Monday May 29

Welcome & Opening Remarks
08:30-10:00 Session I - Chair: Fernando P. Santos
08:30-09:30 Invited Talk: Shimon Whiteson (Oxford)
Efficient & Realistic Simulation for Autonomous Driving
09:30-09:45 Long Talk: Raphael Avalos, Florent Delgrange, Ann Nowe, Guillermo A. Pérez and Diederik M. Roijers
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
09:45-10:00 Long Talk: Felipe Leno Da Silva, Jiachen Yang, Mikel Landajuela, Andre Goncalves, Alexander Ladd, Daniel Faissol and Brenden Petersen
Toward Multi-Fidelity Reinforcement Learning for Symbolic Optimization
10:00-11:00 Coffee Break
11:00-12:30Session II - Chair: Patrick Mannion
11:00-11:15 Long Talk: Minae Kwon, John Agapiou, Edgar Duéñez-Guzmán, Romuald Elie, Georgios Piliouras, Kalesha Bullard and Ian Gemp
Aligning Local Multiagent Incentives with Global Objectives
11:15-11:30 Matthew E. Taylor
Reinforcement Learning Requires Human-in-the-Loop Framing and Approaches
11:30-12:30 Short Talks, 6 minutes each in order:
12:30-14:00 Lunch Break
14:00-15:45Short Talks & Poster Session - Chair: Gaurav Dixit
14:00-14:15 Short Talks, 6 minutes each in order:
14:15-15:45 Poster Session A
15:45-16:30 Coffee Break
16:30-18:00Session III - Chair: Caroline Wang
16:30-16:45 Long Talk: Rory Lipkis and Adrian Agogino
Discovery and Analysis of Rare High-Impact Failure Modes Using Adversarial RL-Informed Sampling
16:45-17:00 Long Talk: Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi and Michael Bowling
Learning to Be Cautious
17:00-18:00 Invited Talk: Peter Stone (UT Austin)
Practical Reinforcement Learning: Lessons from 30 years of Research
~19:00 Social Gathering The Blind Beggar Pub (Google Maps)

Tuesday May 30

08:30-10:00 Session IV - Chair: Connor Yates
08:30-09:30 Invited Talk: Chris Amato (Northeastern University)
Principled and Scalable Multi-Agent Reinforcement Learning
09:30-09:45 Philipp Altmann, Thomy Phan, Fabian Ritz, Claudia Linnhoff-Popien and Thomas Gabor
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
09:45-10:00 Henrik Müller, Lukas Berg and Daniel Kudenko
Using Incomplete and Incorrect Plans to Shape Reinforcement Learning in Long-Sequence Sparse-Reward Tasks
10:00-11:00 Coffee Break
11:00-12:30Session V - Chair: Roxana Rădulescu
11:00-11:15 Guanbao Yu, Umer Siddique and Paul Weng
Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions
11:15-11:30 Long Talk: Manel Rodriguez-Soto, Roxana Radulescu, Juan Antonio Rodriguez Aguilar, Maite Lopez-Sanchez and Ann Nowe
Multi-objective reinforcement learning for guaranteeing alignment with multiple values
11:30-12:30 Short Talks, 6 minutes each in order:
12:30-14:00 Lunch Break
14:00-15:45Short Talks & Poster Session - Chair: Gaurav Dixit
14:00-14:15 Short Talks, 6 minutes each in order:
14:15-15:45 Poster Session B
15:45-16:30 Coffee Break
16:30-18:00Session VI - Chair: Diederik M. Roijer
16:30-16:45 Long Talk: Alain Andres, Lukas Schäfer, Esther Villar-Rodriguez, Stefano Albrecht and Javier Del Ser
Using Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
16:45-17:00 Long Talk: Adam Callaghan, Karl Mason and Patrick Mannion
Evolutionary Strategy guided Reinforcement Learning via MultiBuffer Communication
17:00-18:00 Panel Discussion With Invited Speakers
18:00 Awards & Closing Remarks

Poster Session A - Monday May 29 14:15-15:45

All papers presented on day 1, together with:

Poster Session B - Tuesday May 30 14:15-15:45

All papers presented on day 2, together with:

Accepted Papers

Paper # Authors Title
2Lin Shi and Bei PengCurriculum Learning for Relative Overgeneralization
3Felipe Leno Da Silva, Jiachen Yang, Mikel Landajuela, Andre Goncalves, Alexander Ladd, Daniel Faissol and Brenden PetersenToward Multi-Fidelity Reinforcement Learning for Symbolic Optimization
7Armaan Garg and Shashi Shekhar JhaAutonomous Flood Area Coverage using Decentralized Multi-UAV System with Directed Explorations
8Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi and Michael BowlingLearning to Be Cautious
9Seán Caulfield Curley, Karl Mason and Patrick MannionA Classification Based Approach to Identifying and Mitigating Adversarial Behaviours in Deep Reinforcement Learning Agents
10Malek Mechergui and Sarath SreedharanGoal Alignment: Re-analyzing Value Alignment Problems Using Human-Aware AI
12Qian Shao, Pradeep Varakantham and Shih-Fen ChengCost Constrained Imitation Learning
13Sebastian Schmid and Andreas HarthDistributed Fault Detection For Multi-Agent Systems Based On Vertebrate Foraging
14Bruno Rodrigues, Matthias Knorr, Ludwig Krippahl and Ricardo GonçalvesTowards Explaining Actions of Learning Agents
15Manel Rodriguez-Soto, Roxana Radulescu, Juan Antonio Rodriguez Aguilar, Maite Lopez-Sanchez and Ann NoweMulti-objective reinforcement learning for guaranteeing alignment with multiple values
16Maxime Toquebiau, Nicolas Bredeche, Faïz Ben Amar and Jae Yun Jun KimJoint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Reinforcement Learning
17Minae Kwon, John Agapiou, Edgar Duéñez-Guzmán, Romuald Elie, Georgios Piliouras, Kalesha Bullard and Ian GempAligning Local Multiagent Incentives with Global Objectives
18Ridhima Bector, Hang Xu, Abhay Aradhya, Chai Quek and Zinovi RabinovichShould Importance of an Attack's Future be Determined by its Current Success?
19Ridhima Bector, Hang Xu, Abhay Aradhya, Chai Quek and Zinovi RabinovichPoisoning the Well: Can We Simultaneously Attack a Group of Learning Agents?
20Junlin Lu, Patrick Mannion and Karl MasonInferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach
21Simon Vanneste, Astrid Vanneste, Tom De Schepper, Siegfried Mercelis, Peter Hellinckx and Kevin MetsDistributed Critics using Counterfactual Value Decomposition in Multi-Agent Reinforcement Learning
22Philipp Altmann, Thomy Phan, Fabian Ritz, Claudia Linnhoff-Popien and Thomas GaborDIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
23Callum Rhys Tilbury, Filippos Christianos and Stefano V. AlbrechtRevisiting the Gumbel-Softmax in MADDPG
24Louis Bagot, Lynn D'Eer, Steven Latre, Tom De Schepper and Kevin MetsGPI-Tree Search: Algorithms for Decision-time Planning with the General Policy Improvement Theorem
25Seongmin Kim, Woojun Kim, Jeewon Jeon, Youngchul Sung and Seungyul HanOff-Policy Multi-Agent Policy Optimization with Multi-Step Counterfactual Advantage Estimation
26Xue Yang, Enda Howley and Michael SchukatADT: Agent-based Dynamic Thresholding for Anomaly Detection
27Nicole Orzan, Erman Acar, Davide Grossi and Roxana RădulescuEmergent Cooperation and Deception in Public Good Games
28Henrik Müller, Lukas Berg and Daniel KudenkoUsing Incomplete and Incorrect Plans to Shape Reinforcement Learning in Long-Sequence Sparse-Reward Tasks
29Matthew E. TaylorReinforcement Learning Requires Human-in-the-Loop Framing and Approaches
30Adam Callaghan, Karl Mason and Patrick MannionEvolutionary Strategy guided Reinforcement Learning via MultiBuffer Communication
31Abilmansur Zhumabekov, Daniel May, Tianyu Zhang, Aakash Krishna, Omid Ardakanian and Matthew TaylorEnsembling Diverse Policies Improves Generalizability of Reinforcement Learning Algorithms in Continuous Control Tasks
32Isuri Perera, Frits de Nijs and Julian GarciaLearning to cooperate against ensembles of diverse opponents
33Rory Lipkis and Adrian AgoginoDiscovery and Analysis of Rare High-Impact Failure Modes Using Adversarial RL-Informed Sampling
34Guanbao Yu, Umer Siddique and Paul WengFair Deep Reinforcement Learning with Generalized Gini Welfare Functions
35Archana Vadakattu, Michelle Blom and Adrian PearceStrategy Extraction in Single-agent Games
37Lukas Schäfer, Oliver Slumbers, Stephen McAleer, Yali Du, Stefano Albrecht and David MguniEnsemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
38Robert Loftin, Mustafa Mert Çelikok, Herke Van Hoof, Samuel Kaski and Frans OliehoekUncoupled Learning of Differential Stackelberg Equilibria with Commitments
39Alexandra Cimpean, Pieter Libin, Youri Coppens, Catholijn Jonker and Ann NowéTowards Fairness In Reinforcement Learning
40Danila Valko and Daniel KudenkoIncreasing Energy Efficiency of Bitcoin Infrastructure with Reinforcement Learning and One-shot Path Planning for the Lightning Network
41Nicola Mc Donnell, Enda Howley and Jim DugganQD(λ) Learning: Towards Multi-agent Reinforcement Learning for Learning Communication Protocols
42Mathieu Reymond, Florent Delgrange, Guillermo A. Pérez and Ann NowéWAE-PCN: Wasserstein-autoencoded Pareto Conditioned Networks
44Changxi Zhu, Mehdi Dastani and Shihan WangContinuous Communication with Factorized Policy Gradients in Multi-agent Deep Reinforcement Learning
45Jannis Weil, Johannes Czech, Tobias Meuser and Kristian KerstingKnow your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman
46Sotirios Nikoloutsopoulos, Iordanis Koutsopoulos and Michalis TitsiasPersonalized Federated Learning with Exact Distributed Stochastic Gradient Descent Updates
47Alain Andres, Lukas Schäfer, Esther Villar-Rodriguez, Stefano Albrecht and Javier Del SerUsing Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
48Yash Satsangi and Paniz BehboudianBandit-Based Policy Invariant Explicit Shaping
50Johan Källström and Fredrik HeintzModel-Based Multi-Objective Reinforcement Learning with Dynamic Utility Functions
51Dimitris Michailidis, Willem Röpke, Sennay Ghebreab, Diederik M. Roijers and Fernando P. SantosFairness in Transport Network Design - A Multi-Objective Reinforcement Learning Approach
52Raphael Avalos, Florent Delgrange, Ann Nowe, Guillermo A. Pérez and Diederik M. RoijersThe Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
53Jacobus Smit and Fernando P. SantosLearning Fair Cooperation in Systems of Indirect Reciprocity
54Kartik Bharadwaj, Chandrashekar Lakshminarayanan and Balaraman RavindranContinuous Tactical Optimism and Pessimism
55Prashank Kadam, Ruiyang Xu and Karl LieberherrAccelerating Neural MCTS Algorithms using Neural Sub-Net Structures
56Md. Saiful Islam, Srijita Das, Sai Krishna Gottipati, William Duguay, Cloderic Mars, Jalal Arabneydi, Antoine Fagette, Matthew Guzdial and Matthew E. TaylorWIP: Human-AI interactions in real-world complex environments using a comprehensive reinforcement learning framework
57Ward Gauderis, Fabian Denoodt, Bram Silue, Pierre Vanvolsem and Andries RosseauEfficient Bayesian Ultra-Q Learning for Multi-Agent Games
59Anna Penzkofer, Simon Schaefer, Florian Strohm, Mihai Bâce, Stefan Leutenegger and Andreas BullingInt-HRL: Towards Intention-based Hierarchical Reinforcement Learning
60Alex Goodall and Francesco BelardinelliApproximate Shielding of Atari Agents for Safe Exploration
62Yuan Xue, Megha Khosla and Daniel KudenkoRegulating Action Value Estimation in Deep Reinforcement Learning
63David Radke and Kyle TilburyLearning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams
64Yuxuan Li, Qinglin Liu, Nan Lin and Matthew TaylorWork in Progress: Integrating Human Preference and Human Feedback for Environmentally Adaptable Robotic Learning
65Richard Willis and Michael LuckResolving social dilemmas through reward transfer commitments
67Udari Madhushani, Kevin McKee, John Agapiou, Joel Leibo, Richard Everett, Thomas Anthony, Edward Hughes, Karl Tuyls and Edgar Duenez-GuzmanHeterogeneous Social Value Orientation Improves Meaningful Diversity in Various Incentive Structures
69Luis Thomasini, Lucas Alegre, Gabriel De O. Ramos and Ana L. C. BazzanRouteChoiceGym: a Route Choice Library for Multiagent Reinforcement Learning

Invited Talks

Shimon Whiteson

Affiliation: University of Oxford, Waymo UK

Website: http://whirl.cs.ox.ac.uk/index.html

Title: Title: Efficient & Realistic Simulation for Autonomous Driving

Abstract: In this talk, I will discuss some of the key challenges in performing efficient and realistic simulation for autonomous driving, with a particular focus on how to train simulated agents that model the human road users, such as cars, cyclists, and pedestrians who share the road with autonomous vehicles. I will discuss the need for distributionally realistic agents and present two methods for training hierarchical agents to this end. Finally, I will discuss how the resulting simulator can be used to efficiently train a planning agent to control the autonomous vehicle itself.

Bio: Shimon Whiteson is a Professor of Computer Science at the University of Oxford and the Head of Research at Waymo UK. His research focuses on deep reinforcement learning and learning from demonstration, with applications in robotics and video games. He completed his doctorate at the University of Texas at Austin in 2007. He spent eight years as an Assistant and then an Associate Professor at the University of Amsterdam before joining Oxford as an Associate Professor in 2015. He was awarded a Starting Grant from the European Research Council in 2014, a Google Faculty Research Award in 2017, and a JPMorgan Faculty Award in 2019.

Peter Stone

Affiliation: The University of Texas at Austin

Website: https://www.cs.utexas.edu/~pstone/

Title: Practical Reinforcement Learning: Lessons from 30 years of Research

Abstract: The field of reinforcement learning (RL) has a long history of theoretical results that indicate when RL algorithms *should* work. Throughout this history, there has been a complementary thread of research that tests the theoretical assumptions by seeking to determine when (and how) RL algorithms *do* work in practice. Drawing on 30 years of research results, mostly from the Learning Agents Research Group at UT Austin, this talk will summarize lessons learned about practical RL into four high-level topics: 1) Representation - choosing the algorithm for the problem's representation and adapating the representation to fit the algorithm; 2) Interaction - with other agents and with human trainers; 3) Synthesis - of different algorithms for the same problem and of different concepts in the same algorithm; and 4) Mortality - dealing with the constraint that in most practical settings, opportunities for learning experience are limited. The talk will conclude with a brief introduction to one of the largest ever commercial deployments of an RL agent, Gran Turismo Sophy, which in 2021 won a head-to-head competition against four of the world's best drivers in the Gran Turismo high speed racing game.

Bio: Dr. Peter Stone holds the Truchard Foundation Chair in Computer Science at the University of Texas at Austin. He is Associate Chair of the Computer Science Department, as well as Director of Texas Robotics. In 2013 he was awarded the University of Texas System Regents' Outstanding Teaching Award and in 2014 he was inducted into the UT Austin Academy of Distinguished Teachers, earning him the title of University Distinguished Teaching Professor. Professor Stone's research interests in Artificial Intelligence include machine learning (especially reinforcement learning), multiagent systems, and robotics. Professor Stone received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. He is an Alfred P. Sloan Research Fellow, Guggenheim Fellow, AAAI Fellow, IEEE Fellow, AAAS Fellow, ACM Fellow, Fulbright Scholar, and 2004 ONR Young Investigator. In 2007 he received the prestigious IJCAI Computers and Thought Award, given biannually to the top AI researcher under the age of 35, and in 2016 he was awarded the ACM/SIGAI Autonomous Agents Research Award. Professor Stone co-founded Cogitai, Inc., a startup company focused on continual learning, in 2015, and currently serves as Executive Director of Sony AI America.

Christopher Amato

Affiliation:Northeastern University

Website: https://www.ccs.neu.edu/home/camato/index.html

Talk Title: Principled and Scalable Multi-Agent Reinforcement Learning

Abstract: The decreasing cost and increasing sophistication of hardware has created new opportunities for applications where teams of agents (e.g., robots, autonomous vehicles, AI software) can be deployed to solve complex problems. However, if such systems are to become widely deployable, we must develop the control and coordination methods that allow the agents to perform well in large real-world domains with significant uncertainty and communication limitations. I’ll first talk about some of the fundamental challenges and misunderstandings of multi-agent reinforcement learning along with promising future directions. In particular, I’ll discuss how 1) centralized critics are not strictly better than decentralized critics (and can be worse), and 2) state-based critics are unsound and work well only on a subclass of problems. I’ll also talk about some of our work on scalable reinforcement learning methods for asynchronous multi-agent systems.

Bio: Christopher Amato is an Associate Professor at Northeastern University where he leads the Lab for Learning and Planning in Robotics. He has published over 60 papers in leading artificial intelligence, machine learning and robotics conferences (including winning a best paper prize at AAMAS-14 and being nominated for the best paper at RSS-15, AAAI-19, AAMAS-21 and MRS-21). He has also won several awards such as Amazon Research Awards and an NSF CAREER Award. His research focuses on reinforcement learning in partially observable and multi-agent/multi-robot systems.

Awards

Best Paper Award

We are pleased to announce the best paper of ALA 2023, sponsored by Neural Computing & Applications, is Fair Deep Reinforcement Learning with Generalized Gini Welfare Functions, by Guanbao Yu, Umer Siddique and Paul Weng!

Programe Committee

  • Adrian Agogino, University of California Santa Cruz, USA
  • Lucas Alegre, UFRGS, BRA
  • Muhammad Arrasy-Rahman, The University of Texas at Austin, USA
  • Raphael Avalos, Vrije Universiteit Brussel, BEL
  • Angel Ayala, Universidade de Pernambuco, BRA
  • Wolfram Barfuss, Tuebingen AI Center, University of Tuebingen, GER
  • Rodrigo Bonini, Federal University of ABC (UFABC), BRA
  • Roland Bouffanais, University of Ottawa, CAN
  • Adam Callaghan, University of Galway, IRL
  • Mustafa Mert Çelikok, Aalto University FIN
  • Raphael Cobe, Sao Paulo State University, BRA
  • Francisco Cruz, Deakin, AUS
  • Jiaxun Cui, The University of Texas at Austin, USA
  • Felipe leno Da silva, LLNL, USA
  • Gaurav Dixit, Oregon State University, USA
  • Florian Felten, SnT, University of Luxembourg, LUX
  • Elias Fernández Domingos, MLG, Université Libre de Bruxelles; AI-lab, Vrije Universiteit Brussels, BEL
  • Julian Garcia, Monash University, AUS
  • Ruben Glatt, Lawrence Livermore National Laboratory, USA
  • Brent Harrison, University of Kentucky, USA
  • Conor F Hayes, NUI Galway, IRL
  • Thommen Karimpanal George, Deakin University, AUS
  • Sammie Katt, Northeastern University, USA
  • Matt Knudson, NASA, USA
  • Johan Källström, Linköping University, SWE
  • Mikel Landajuela Larma, Lawrence Livermore National Laboratory, USA
  • Junlin Lu, University of Galway, IRL
  • Udari Madhushani, Princeton University, USA
  • Patrick Mannion, National University of Ireland Galway, IRL
  • Karl Mason, University of Galway, IRL
  • Nicolás Navarro-Guerrero, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), GER
  • Frans Oliehoek, Delft University of Technology, NLD
  • Bei Peng, University of Liverpool, GBR
  • Roxana Radulescu, Vrije Universiteit Brussel, BEL
  • Gabriel De O. Ramos, Universidade do Vale do Rio dos Sinos, BRA
  • Mathieu Reymond, Vrije Universiteit Brussel, BEL
  • Diederik M. Roijers, Vrije Universiteit Brussel & HU University of Applied Sciences Utrecht, BEL
  • Willem Röpke, Vrije Universiteit Brussel, BEL
  • Fernando Santos, University of Amsterdam, NLD
  • Miguel Solis, Universidad Andrés Bello, CHL
  • Denis Steckelmacher, Vrije Universiteit Brussel, BEL
  • Paolo Turrini, University of Warwick, GBR
  • Peter Vamplew, Federation University Australia, AUS
  • Miguel Vasco, INESC-ID, PRT
  • Vítor V. Vasconcelos, University of Amsterdam, NLD
  • Caroline Wang, U Texas, USA
  • Connor Yates, Oregon State University, USA
  • Junpei Zhong, The Hong Kong Polytechnic University, HKG
  • Changxi Zhu, Utrecht University, NLD

Organization

This year's workshop is organised by: Senior Steering Committee Members:
  • Enda Howley (University of Galway, IE)
  • Daniel Kudenko (University of York, UK)
  • Patrick Mannion (University of Galway, IE)
  • Ann Nowé (Vrije Universiteit Brussel, BE)
  • Sandip Sen (University of Tulsa, US)
  • Peter Stone (University of Texas at Austin, US)
  • Matthew Taylor (University of Alberta, CA)
  • Kagan Tumer (Oregon State University, US)
  • Karl Tuyls (University of Liverpool, UK)

Contact

If you have any questions about the ALA workshop, please contact the organizers at:
ala.workshop.2023 AT gmail.com

For more general news, discussion, collaboration and networking opportunities with others interested in Adaptive Learning Agents then please join our Linkedin Group