ALA 2024

ALA 2024 - Workshop at AAMAS 2024

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 sixteenth year. Previous editions of this workshop may be found at the following urls:

• ALA-23
• ALA-22
• ALA-21
• ALA-20
• ALA-19
• ALA-18
• ALA-17
• ALA-16
• ALA-15
• ALA-14
• ALA-13
• ALA-12
• ALA-11
• ALA-10
• ALA-09
• ALAMAS+ALAg-08
• ALAg-07
• Earlier editions

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 using reasoning modules like negotiation, trust, coordination, etc..
• Supervised and semi-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
• Human-in-the-loop learning 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: 5 February 2024 9 February 2024   12 February 2024
• Notification of acceptance: 4 March 2024
• Camera-ready copies: 25 March 2024   19 April 2024
• Workshop: 6 - 7 May 2024

Submission Details

Papers can be submitted through OpenReview.

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 2024. Keeping with previous ALA guidelines, papers are limited to 8 pages plus references. 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, authors need to also append the received reviews and a pdfdiff.

All submissions will be peer-reviewed (doubl-blind). Accepted work will be allocated time for poster and possibly oral presentation during the workshop. In line with AAMAS, the workshop will be fully offline. 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 2024, please be sure to remove the AAMAS copyright block, citation information and running headers. You leave the submission id in the AAMAS template empty as we use OpenReview instead of easy chair. Please replace the AAMAS copyright block in the main.tex file from the AAMAS template with the following:

    \setcopyright{none}
    \acmConference[ALA '24]{Proc.\@ of the Adaptive and Learning Agents Workshop (ALA 2024)}
    {May 6-7, 2024}{Auckland, \url{https://ala2024.github.io/}}{Avalos, Milec, M\"uller, Wang, Yates (eds.)}
    \copyrightyear{2024}
    \acmYear{2024}
    \acmDOI{}
    \acmPrice{}
    \acmISBN{}
    \settopmatter{printacmref=false}
                            

For the submission of the camera-ready paper make sure to submit the deanonymized version with the replaced copyright block above.

Journal Special Issue

We are delighted to announce that extended versions of all original contributions at ALA 2024 will be eligible for inclusion in a special issue of the Springer journal Neural Computing and Applications (Impact Factor 6.0). The deadline for submitting extended papers will be 15 November 2024.

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

Program

All times are presented in local Auckland time.

Monday May 6

	Welcome & Opening Remarks
09:00-10:00	Session I - Chair: TBD
09:00-10:00	Invited Talk: Marc Lanctot (Deepmind) Game-Theoretic Approaches to Adaptive Learning Agents in Strategic Environments
10:00-10:45	Coffee Break
10:45-12:30	Session II - Chair: TBD
10:45-11:05	Long Talk: Bram M. Renting, Holger Hoos, Catholijn M Jonker Multi-Agent Meeting Scheduling: A Negotiation Perspective
11:05-11:25	Long Talk: Pascal Van der Vaart, Neil Yorke-Smith, Matthijs T. J. Spaan Bayesian Ensembles for Exploration in Deep Q-Learning
11:25-11:45	Long Talk: Hei Yi Mak, Flint Xiaofeng Fan, Luca A Lanzendörfer, Cheston Tan, Wei Tsang Ooi, Roger Wattenhofer CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
11:45-12:05	Long Talk: Sunghoon Hong, Whiyoung Jung, Deunsol Yoon, Kanghoon Lee, Woohyung Lim Agent-Oriented Centralized Critic for Asynchronous Multi-Agent Reinforcement Learning
12:05-12:25	Long Talk: Nicole Orzan, Erman Acar, Davide Grossi, Roxana Rădulescu Learning in Public Goods Games with Non-Linear Utilities: a Multi-Objective Approach
12:30-14:00	Lunch Break
14:00-16:00	Session III & Poster Session - Chair: TBD
14:00-14:20	Long Talk: Zun Li, Michael Wellman A Meta-Game Evaluation Framework for Deep Multiagent Reinforcement Learning
14:20-14:40	Long Talk: Simone Drago, Marco Mussi, Marcello Restelli, Alberto Maria Metelli Intermediate Observations in Factored-Reward Bandits
14:40-14:55	Short Talks, 5 minutes each in order: Short Talk: Ram Rachum, Yonatan Nakar, William Tomlinson, Nitay Alon, Reuth Mirsky Emergent Dominance Hierarchies in Reinforcement Learning Agents Short Talk: David Milec, Ondrej Kubicek, Viliam Lisý Continual Depth-limited Responses for Computing Counter-strategies in Sequential Games Short Talk: Timothy Flavin, Sandip Sen A Bayesian Approach to Learning Command Hierarchies for Zero-Shot Multi-Agent Coordination
15:00-16:00	Poster Session A
16:00-16:30	Coffee Break
16:30-17:30	Session IV - Chair: TBD
16:30-17:30	Invited Talk: Michael Wellman Artificial Intelligence and its Implications for Financial Markets

Tuesday May 7

09:00-10:00	Session V - Chair: TBD
09:00-10:00	Invited Talk: TBA
10:00-10:45	Coffee Break
10:45-12:30	Session VI - Chair: TBD
10:45-11:05	Long Talk: Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Perolat Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
11:05-11:25	Long Talk: Jérôme Botoko Ekila, Jens Nevens, Lara Verheyen, Katrien Beuls, Paul Van Eecke Decentralised Emergence of Robust and Adaptive Linguistic Conventions in Populations of Autonomous Agents Grounded in Continuous Worlds
11:25-11:45	Long Talk: Alexandra Cimpean, Catholijn M Jonker, Pieter Jules Karel Libin, Ann Nowe A Group And Individual Aware Framework For Fair Reinforcement Learning
11:45-12:05	Long Talk: Jonathan G. Faris, Conor F. Hayes, Andre R Goncalves, Kayla G. Sprenger, Daniel faissol, Brenden K. Petersen, Mikel Landajuela, Felipe Leno da Silva Pareto Front Training For Multi-Objective Symbolic Optimization
12:05-12:25	Long Talk: Jérôme Arjonilla, Tristan Cazenave and Abdallah Saffidine Perfect Information Monte Carlo with postponing reasoning
12:30-14:00	Lunch Break
14:00-15:45	Session VII & Poster Session - Chair: TBD
14:00-14:20	Long Talk: Radovan Haluška and Martin Schmid Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents
14:20-14:50	Short Talks, 5 minutes each in order: Kyle Crandall, Connor Yates, Corbin Wilhelmi Lyapunov Guarantees for Learned Policies Rolando Fernandez, Garrett Warnell, Derrik E. Asher, Peter Stone Multi-Agent Synchronization Tasks Argha Boksi, Balaraman Ravindran Inter-agent Transfer Learning in Communication-constrained Settings : A Student Initiated Advising Approach Brian Burns, Aravind Sundaresan, Pedro Sequeira, Vidyasagar Sadhu Learning Sensor Control for Information Gain in Dynamic, Partially Observed and Sparsely Sampled Environments Arnau Mayoral Macau, Manel Rodriguez-Soto, Maite López-Sánchez, Juan Antonio Rodriguez Aguilar, Enrico Marchesini, Alessandro Farinelli An approximate process for designing ethical environments with multi-agent reinforcement learning Jérôme Arjonilla, Tristan Cazenave and Abdallah Saffidine Enhancing Reinforcement Learning Through Guided Search
15:00-16:00	Poster Session B
16:00-16:30	Coffee Break
16:30-17:30	General Discussion
17:30	Awards & Closing Remarks

Poster Session A - Monday May 6 15:00-16:00

All papers presented on day 1.

Poster Session B - Tuesday May 7 15:00-16:00

All papers presented on day 2.

Accepted Papers

Paper #	Authors	Title
2	Ram Rachum, Yonatan Nakar, William Tomlinson, Nitay Alon, Reuth Mirsky	Emergent Dominance Hierarchies in Reinforcement Learning Agents
3	Simone Drago, Marco Mussi, Marcello Restelli, Alberto Maria Metelli	Intermediate Observations in Factored-Reward Bandits
4	Kyle Crandall, Connor Yates, Corbin Wilhelmi	Lyapunov Guarantees for Learned Policies
5	Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Perolat	Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
7	Pascal Van der Vaart, Neil Yorke-Smith, Matthijs T. J. Spaan	Bayesian Ensembles for Exploration in Deep Q-Learning
9	Jérôme Botoko Ekila, Jens Nevens, Lara Verheyen, Katrien Beuls, Paul Van Eecke	Decentralised Emergence of Robust and Adaptive Linguistic Conventions in Populations of Autonomous Agents Grounded in Continuous Worlds
10	Hei Yi Mak, Flint Xiaofeng Fan, Luca A Lanzendörfer, Cheston Tan, Wei Tsang Ooi, Roger Wattenhofer	CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
11	Sunghoon Hong, Whiyoung Jung, Deunsol Yoon, Kanghoon Lee, Woohyung Lim	Agent-Oriented Centralized Critic for Asynchronous Multi-Agent Reinforcement Learning
13	David Milec, Ondrej Kubicek, Viliam Lisý	Continual Depth-limited Responses for Computing Counter-strategies in Sequential Games
14	Rolando Fernandez, Garrett Warnell, Derrik E. Asher, Peter Stone	Multi-Agent Synchronization Tasks
15	Nicole Orzan, Erman Acar, Davide Grossi, Roxana Rădulescu	Learning in Public Goods Games with Non-Linear Utilities: a Multi-Objective Approach
16	Argha Boksi, Balaraman Ravindran	Inter-agent Transfer Learning in Communication-constrained Settings : A Student Initiated Advising Approach
17	Timothy Flavin, Sandip Sen	A Bayesian Approach to Learning Command Hierarchies for Zero-Shot Multi-Agent Coordination
18	Brian Burns, Aravind Sundaresan, Pedro Sequeira, Vidyasagar Sadhu	Learning Sensor Control for Information Gain in Dynamic, Partially Observed and Sparsely Sampled Environments
22	Alexandra Cimpean, Catholijn M Jonker, Pieter Jules Karel Libin, Ann Nowe	A Group And Individual Aware Framework For Fair Reinforcement Learning
23	Bram M. Renting, Holger Hoos, Catholijn M Jonker	Multi-Agent Meeting Scheduling: A Negotiation Perspective
24	Arnau Mayoral Macau, Manel Rodriguez-Soto, Maite López-Sánchez, Juan Antonio Rodriguez Aguilar, Enrico Marchesini, Alessandro Farinelli	An approximate process for designing ethical environments with multi-agent reinforcement learning
25	Jonathan G. Faris, Conor F. Hayes, Andre R Goncalves, Kayla G. Sprenger, Daniel faissol, Brenden K. Petersen, Mikel Landajuela, Felipe Leno da Silva	Pareto Front Training For Multi-Objective Symbolic Optimization
28	Zun Li, Michael Wellman	A Meta-Game Evaluation Framework for Deep Multiagent Reinforcement Learning
29	Jérôme Arjonilla, Tristan Cazenave and Abdallah Saffidine	Enhancing Reinforcement Learning Through Guided Search
32	Jérôme Arjonilla, Tristan Cazenave and Abdallah Saffidine	Perfect Information Monte Carlo with postponing reasoning
33	Radovan Haluška and Martin Schmid	Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents

Invited Talks

Marc Lanctot

Title: Game-Theoretic Approaches to Adaptive Learning Agents in Strategic Environments

Abstract: In this talk, I will motivate the importance of adaptation in general environments and the role that game theory could play along the way. I will briefly talk about different ways of assessing the performance of agents that have been adopted by the community. Then, I will outline a few potential environments and evaluation schemes that could be used for such purposes. I will then talk in detail about a specific direction that embraces Meta-RL and Bayesian reasoning in a practical way, through the use of game-theoretic training regimes and applications to multiplayer games with partial observability. As a case study, I will show recent results on agents that have learned to play negotiation games through combined reinforcement learning and search, as well as observations from their interactions with human participants.

Michael Wellman

Title: Artificial Intelligence and its Implications for Financial Markets

Abstract: The rapid advancement of surprisingly capable AI is raising questions about AI’s impact on virtually all aspects of our economy and society. The nexus of AI and Finance is especially salient, building on the impact AI has already had on trading and other financial domains. New AI developments could exacerbate market manipulation, and otherwise create loopholes in regulatory regimes. We need improved ways to anticipate AI impacts and evaluate capabilities of advanced interactive AI.

Programe Committee

• Adrian Agogino, University of Texas, Austin, USA
• Lucas Nunes Alegre, Federal University of Rio Grande do Sul, BRA
• Hicham Azmani, Vrije Universiteit Brussel, BEL
• Alexandra Cimpean, Vrije Universiteit Brussel, BEL
• Valentin Colliard, LIP6, FRA
• Elena Congeduti, Delft University of Technology, NLD
• Kyle Crandall, US Naval Research Lab, USA
• Jiaxun Cui, The University of Texas at Austin, USA
• Ryan D'Orazio, Université de Montréal , CAN
• Gaurav Dixit, Oregon State University, USA
• Simone Drago, Polytechnic Institute of Milan, ITA
• Ishan Durugkar, Sony AI Inc., USA
• Jérôme Botoko Ekila, Vrije Universiteit Brussel, BEL
• Flint Xiaofeng Fan, National University of Singapore, SGP
• Jonathan G. Faris, University of Colorado at Boulder, USA
• Florian Felten, University of Luxemburg, LUX
• Rolando Fernandez, University of Texas at Austin, USA
• Timothy Flavin, University of Tulsa, USA
• Tim French, University of Western Australia, AUS
• Julian Garcia, Monash University, AUS
• Conor F. Hayes, Lawrence Livermore National Labs, USA
• Whiyoung Jung, LG AI Research, KOR
• Thommen Karimpanal George, Deakin University, AUS
• Sammie Katt, Aalto University, FIN
• Ondřej Kubíček, Czech Technical University , CZE
• Marc Lanctot, Google DeepMind, CAN
• Pieter Libin, Vrije Universiteit Brussel, BEL
• Chuang-Chieh Lin, Tamkang University, TPE
• Viliam Lisý , Czech Technical University in Prague, CZE
• Robert Loftin, University of Sheffield, GBR
• Junlin Lu, National University of Ireland, Galway, IRL
• Simon Lucas, Queen Mary University of London , GBR
• Patrick MacAlpine, Sony AI, USA
• Karl Mason, University of Galway, IRL
• Stephanie Milani, Carnegie Mellon University , USA
• David Milec, Czech Technical University in Prague, CZE
• Nicole Orzan, University of Groningen, NLD
• Bei Peng, University of Liverpool, GBR
• Diego Perez Liebana , Queen Mary University of London , GBR
• Gang Qiao, Siemens Healthineers, USA
• Ram Rachum, Bar-Ilan University, ISR
• Roxana Rădulescu, Vrije Universiteit Brussel, BEL
• Arrasy Rahman, University of Texas at Austin, USA
• Balaraman Ravindran, Indian Institute of Technology Madras, IND
• Bram M. Renting, Leiden University, NLD
• Mathieu Reymond, Vrije Universiteit Brussel, BEL
• Manel Rodriguez-Soto, Artificial Intelligence Research Institute, Spanish National Research Council, ESP
• Diederik Roijers, University of Amsterdam, NLD
• Willem Röpke, Vrije Universiteit Brussel, BEL
• Andries Rosseau, Vrije Universiteit Brussel, BEL
• Sam Sokota, Carnegie Mellon University , USA
• Michal Sustr, AIC FEE CTU , CZE
• Paolo Turrini, University of Warwick, GBR
• Peter Vamplew, Federation University Australia, AUS
• Pascal Van der Vaart, Delft University of Technology, NLD
• Timothy Verstraeten, Vrije Universiteit Brussel, BEL
• Brian Zhang, Carnegie Mellon University , USA
• Weiye Zhao, Carnegie Mellon University , USA
• Junpei Zhong, Hong Kong Polytechnic University, HK

Organization

This year's workshop is organised by:

• Raphael Avalos (Vrije Universiteit Brussel, BE)
• David Milec (Czech Technical University in Prague, CZ)
• Henrik Müller (Leibniz University Hannover, DE)
• Caroline Wang (University of Texas at Austin, US)
• Connor Yates (U.S. Naval Research Laboratory, US)

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.2024 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

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