News

ALA 2021 - Workshop at AAMAS 2021

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 thirteenth 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: 10 February 2021   24 February 2021 23:59 UTC
  • Notification of acceptance: 10 March 2021   24 March 2021
  • Camera-ready copies: 24 March 2021   26 April 2021
  • Workshop: 3 - 4 May 2021

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 2021. 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.

Journal Special Issue

We are delighted to announce that extended versions of all original contributions at ALA 2021 will be eligible for inclusion in a special issue of the Springer journal Neural Computing and Applications (Impact Factor 4.213). The deadline for submitting extended papers will be 15 September 2021.

NCA

We will post further details about the submission process and expected publication timeline here after the workshop.

Program

Except for the invited talks, ALA 2021 will take place in an asynchronous manner. In order to facilitate discussions over all the contributions as well as social interactions, we invite all authors and participants to join our Slack workspace.

In order to organise discussions, we ask authors/participants to create channels with the name paper-#. We have added below a unique number for each contribution.

The ALA 2021 live sessions will take place via Zoom. The links to the live sessions are available on the AAMAS Workshops and Tutorials Underline platform. In order to access the platform, registration is required and it is free.

Monday, 3 May

14:50 - 15:00 BST Welcome & Opening Remarks
15:00 - 16:00 BST Discussion Panel
Topic: Links between Industry, Academia and Governments in AI development
Chair: Matthew Gombolay (Georgia Institute of Technology)
Panelists:
  • Athirai A. Irissappane (University of Washington)
  • Peter Vamplew (Federation University Australia)
  • Ruben Glatt (Lawrence Livermore National Laboratory)
  • Ana Paula Appel (IBM Brazil)
16:00 - 17:00 BST Invited Talk: Naomi Ehrich Leonard
Opinion Dynamics and Reinforcement Learning in Multi-agent Games

Tuesday, 4 May

15:00 - 16:00 BST Invited Talk: Marc G. Bellemare
Autonomous navigation of superpressure balloons using reinforcement learning
17:00 - 18:00 BST Invited Tutorial: Matthew Gombolay
Interpretable Models for Interactive Multi-Robot Learning
18:00 - 18:15 BST Awards, closing remarks and ALA 2022
Best Paper Award:
Jacopo Castellini, Sam Devlin, Frans A. Oliehoek and Rahul Savani,
Difference Rewards Policy Gradients

Accepted Papers

Long Talks

Paper # Details Title Authors
5[Paper][Video]
[Slides]
Guaranteeing the Learning of Ethical Behaviour through Multi-Objective Reinforcement LearningManel Rodríguez-Soto, Juan Antonio Rodriguez Aguilar and Maite Lopez-Sanchez
9[Paper][Video]
[Slides]
Difference Rewards Policy GradientsJacopo Castellini, Sam Devlin, Frans A. Oliehoek and Rahul Savani
11[Paper][Video]
[Slides]
A Multi-Arm Bandit Approach To Subset Selection Under ConstraintsAyush Deva, Kumar Abhishek and Sujit Gujar
24[Paper][Video]
[Slides]
Self-Transfer Reinforcement LearningHélène Plisnier, Denis Steckelmacher and Ann Nowé
26[Paper][Video]
[Slides]
Sample-Efficient Reinforcement Learning for Continuous Actions with Continuous BDPIDenis Steckelmacher, Hélène Plisnier and Ann Nowe
27[Paper][Video]
[Slides]
Combining Off and On-Policy Training in Model-Based Reinforcement LearningAlexandre Borges and Arlindo Oliveira
28[Paper][Video]
[Slides]
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement LearningVolodymyr Tkachuk, Sriram G. Subramanian and Matthew E. Taylor
30[Paper][Video]
[Slides]
Towards Open Ad Hoc Teamwork Using Graph-based Policy LearningMuhammad Arrasy Rahman, Niklas Hopner, Filippos Christianos and Stefano V. Albrecht
33[Paper][Video]
[Slides]
Risk-Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree SearchConor F Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley and Patrick Mannion
39[Paper][Video]
[Slides]
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum GamesStratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras and Lillian Ratliff
40[Paper][Video]
[Slides]
Temporal Difference and Return Optimism in Cooperative Multi-Agent Reinforcement LearningMark Rowland, Shayegan Omidshafiei, Daniel Hennes, Will Dabney, Andrew Jaegle, Paul Muller, Julien Perolat and Karl Tuyls
44[Paper][Video]
[Slides]
Comparative Evaluation of Cooperative Multi-Agent Deep Reinforcement Learning AlgorithmsGeorgios Papoudakis, Filippos Christianos, Lukas Schäfer and Stefano Albrecht

Short Talks

Paper # Details Title Authors
8[Paper][Video]
[Slides]
Is the Cerebellum a Model-Based Reinforcement Learning Agent?Bharath Masetty, Reuth Mirsky, Ashish Deshpande, Michael Mauk and Peter Stone
14[Paper][Video]
[Slides]
Exploring the Impact of Tunable Agents in Sequential Social DilemmasDavid O'Callaghan and Patrick Mannion
18[Paper][Video]
[Slides]
Differential Privacy Meets Maximum-weight MatchingPanayiotis Danassis, Aleksei Triastcyn and Boi Faltings
20[Paper][Video]
[Slides]
Communication Strategies in Multi-Objective Normal-Form GamesWillem Röpke, Roxana Rădulescu, Diederik Roijers and Ann Nowé
29[Paper][Video]
[Slides]
Robustness to Adversarial Attacks in Learning-Enabled ControllersZikang Xiong, Joe Eappen, He Zhu and Suresh Jagannathan
34[Paper][Video]
[Slides]
Scaling Multi-Agent Reinforcement Learning with Selective Parameter SharingFilippos Christianos, Georgios Papoudakis, Muhammad Arrasy Rahman and Stefano Albrecht
36[Paper][Video]
[Slides]
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational BanditsKaushik Roy, Qi Zhang, Manas Gaur and Amit Sheth
38[Paper][Video]
[Slides]
Online Learning in Periodic Zero-Sum Games: von Neumann vs PoincareTanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras and Lillian Ratliff
43[Paper][Video]
[Slides]
Dominance Criteria and Solution Sets for the Expected Scalarised ReturnsConor F Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley and Patrick Mannion
48[Paper][Video]
[Slides]
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy GradientsBozhidar Vasilev, Tarun Gupta, Bei Peng and Shimon Whiteson
49[Paper][Video]
[Slides]
Particle Value Functions in Imperfect Information GamesMichal Sustr, Vojtech Kovarik and Viliam Lisy
53[Paper][Video]
[Slides]
Value-Based Reinforcement Learning for Sequence-to-Sequence ModelsFabian Retkowski
55[Paper][Video]
[Slides]
LTLf-based Reward Shaping for Reinforcement LearningMahmoud Elbarbari, Kyriakos Efthymiadis, Bram Vanderborght and Ann Nowé
58[Paper][Video]
[Slides]
Truly Black-box Attack on Reinforcement Learning via Environment PoisoningHang Xu and Rabinovich Zinovi
62[Paper][Video]
[Slides]
Data-Driven Reinforcement Learning for Virtual Character Animation ControlVihanga Gamage, Cathy Ennis and Robert Ross

Spotlight

Paper # Details Title Authors
4[Paper][Video]
[Slides]
A Second-Order Adaptive Cognitive Agent Model for Emotion Regulation in Addictive Social Media BehaviourElisabeth Fokker, Xinran Zong and Jan Treur
6[Paper][Video]
[Slides]
RLupus: Cooperation through Emergent Communication in The Werewolf Social Deduction GameNicolo Brandizzi, Luca Iocchi and Davide Grossi
16[Paper][Video]
[Slides]
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent BehaviorHossein Haeri, Reza Ahmadzadeh and Kshitij Jerath
17[Paper][Video]
[Slides]
Predicting Aircraft Trajectories via Imitation LearningTheocharis Kravaris, Alevizos Bastas and George Vouros
19[Paper][Video]
[Slides]
LIEF: Learning to Influence through Evaluative FeedbackRamona Merhej and Mohamed Chetouani
21[Paper][Video]
[Slides]
Adaptive learning for financial markets mixing model-based and model-free RLEric Benhamou, David Saltiel, Serge Tabachnik, Sui Kai Wong and François Chareyron
23[Paper][Video]
[Slides]
Reasoning about Human Behavior in Ad-Hoc TeamworkJennifer Suriadinata, William Macke, Reuth Mirsky and Peter Stone
25[Paper][Video]
[Slides]
Robust Online Planning with Imperfect ModelsMaxim Rostov and Michael Kaisers
31[Paper][Video]
[Slides]
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time WindowsNazneen N Sultana, Vinita Baniwal, Ansuma Basumatary, Piyush Mittal, Supratim Ghosh and Harshad Khadilkar
35[Paper][Video]
[Slides]
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingNikunj Gupta, Gopalakrishnan Srinivasaraghavan, Swarup Mohalik, Nishant Kumar and Matthew E. Taylor
37[Paper][Video]
[Slides]
Work-in-progress: Comparing Feedback Distributions in Limited Teacher-Student SettingsCalarina Muslimani, Kerrick Johnstonbaugh and Matthew Taylor
41[Paper][Video]
[Slides]
Searching with Opponent-AwarenessTimy Phan
45[Paper][Video]
[Slides]
Deep reinforcement learning for rehabilitation planning of water pipes networkZaharah A. Bukhsh, Nils Jansen and Hajo Molegraaf
50[Paper][Video]
[Slides]
Latent Property State Abstraction For Reinforcement learningJohn Burden, Daniel Kudenko and Sajjad Kamali Siahroudi
51[Paper][Video]
[Slides]
Work-in-progress: Revisiting Experience Replay in Non-stationary EnvironmentsDerek Li, Andrew Jacobsen and Adam White
52[Paper][Video]
[Slides]
Evaluating household-pooled universal testing to control COVID-19 epidemics, using an individual-based modelPieter Libin, Lander Willen, Timothy Verstraeten, Andrea Torneri, Joris Vanderlocht and Niel Hens
57[Paper][Video]
[Slides]
Graph Learning based Generation of Abstractions for Reinforcement LearningYuan Xue, Daniel Kudenko and Megha Khosla
61[Paper][Video]
[Slides]
NovelGridworlds: A Benchmark Environment for Detecting and Adapting to Novelties in Open WorldsShivam Goel, Gyan Tatiya, Matthias Scheutz and Jivko Sinapov

Invited Talks

Marc G. Bellemare

Affiliations:
      Research Scientist, Google Research (Brain team)
      Adjunct Professor, McGill University
      Canada CIFAR AI Chair, Mila

Website: http://www.marcgbellemare.info

Talk Title: Autonomous navigation of superpressure balloons using reinforcement learning

Abstract: Efficiently navigating a superpressure balloon in the stratosphere requires the integration of a multitude of cues, such as wind speed and solar elevation, and the process is complicated by forecast errors and sparse wind measurements. Coupled with the need to make decisions in real time, these factors rule out the use of conventional control techniques. This talk describes the use of reinforcement learning to create a high-performing flight controller for Loon superpressure balloons. Our algorithm uses data augmentation and a self-correcting design to overcome the key technical challenge of reinforcement learning from imperfect data, which has proved to be a major obstacle to its application to physical systems. We deployed our controller to station Loon balloons at multiple locations across the globe, including a 39-day controlled experiment over the Pacific Ocean. Analyses show that the controller outperforms Loon’s previous algorithm and is robust to the natural diversity in stratospheric winds. These results demonstrate that reinforcement learning is an effective solution to real-world autonomous control problems in which neither conventional methods nor human intervention suffice, offering clues about what may be needed to create artificially intelligent agents that continuously interact with real, dynamic environments.

Bio: Marc G. Bellemare leads the reinforcement learning efforts at Google Research in Montreal and holds a Canada CIFAR AI Chair at the Quebec Artificial Intelligence Institute (Mila). He received his Ph.D. from the University of Alberta, where he developed the highly-successful Arcade Learning Environment benchmark. From 2013 to 2017 he held the position of research scientist at DeepMind in London, UK, where he made major contributions to deep reinforcement learning, in particular pioneering the distributional method. Marc G. Bellemare is also a CIFAR Learning in Machines & Brains Fellow and an adjunct professor at McGill University.

Naomi Ehrich Leonard

Affiliation: Princeton University

Website: https://naomi.princeton.edu

Talk Title: Opinion Dynamics and Reinforcement Learning in Multi-agent Games

Abstract: I will discuss a recently proposed model of opinion dynamics and its use, as a model of reinforcement learning with distributed information sharing, in the study of multi-agent strategic interaction. Our model describes continuous-time opinion dynamics for an arbitrary number of agents that communicate over a network and form real-valued opinions about an arbitrary number of options. Many models in the literature update agent opinions using a weighted average of their neighbors’ opinions. Our model generalizes these models by applying a sigmoidal saturation function to opinion exchanges. This makes the update fundamentally nonlinear: opinions form through a bifurcation yielding multi-stability of network opinion configurations. Leveraging idealized symmetries in the system, qualitative behavioral regimes can be distinguished explicitly in terms of just a few parameters. I will show how to interpret the opinion dynamics as a model of reinforcement learning in multi-agent finite games and discuss implications for games including the prisoner’s dilemma and the traveler’s dilemma. This is joint work with Anastasia Bizyaeva, Alessio Franci, Shinkyu Park, and Yunxiu Zhou, and based on papers https://arxiv.org/abs/2009.04332v2 and https://arxiv.org/abs/2103.14764.

Bio: Naomi Ehrich Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty member of the Program in Applied and Computational Mathematics at Princeton University. She is Director of Princeton's Council on Science and Technology and affiliated faculty member of the Princeton Neuroscience Institute and Program on Quantitative and Computational Biology. She received her BSE in Mechanical Engineering from Princeton University and her PhD in Electrical Engineering from the University of Maryland. Leonard is a MacArthur Fellow, a member of the American Academy of Arts and Sciences, and a Fellow of the ASME, IEEE, IFAC, and SIAM. Prof. Leonard's background includes feedback control theory, nonlinear dynamics, geometric mechanics, and robotics, where she has made contributions both to theory and to application. She studies and designs complex, dynamical systems comprised of many interacting agents, including, for example, animais, robots, and/or humans that move, sense, and decide together. Her research program emphasizes the development of analytically tractable mathematical models of collective dynamics that provide the systematic means to examine the role of feedback (responsive behavior), network interconnection (who is communicating with whom), heterogeneity (individual differences) in the behavior, learning, and resilience of groups in changing environments.

Matthew Gombolay

Affiliation: Georgia Institute of Technology

Website: https://core-robotics.gatech.edu/people/matthew-gombolay/

Talk Title: Interpretable Models for Interactive Multi-Robot Learning

Bio: Dr. Matthew Gombolay is an Assistant Professor of Interactive Computing at the Georgia Institute of Technology. He received a B.S. in Mechanical Engineering from the Johns Hopkins University in 2011, an S.M. in Aeronautics and Astronautics from MIT in 2013, and a Ph.D. in Autonomous Systems from MIT in 2017. Gombolay’s research interests span robotics, AI/ML, human-robot interaction, and operations research. Between defending his dissertation and joining the faculty at Georgia Tech, Dr. Gombolay served as technical staff at MIT Lincoln Laboratory, transitioning his research to the U.S. Navy and earning a R&D 100 Award. His publication record includes a best paper award from the American Institute for Aeronautics and Astronautics, a finalist for best student paper at the American Controls Conference, and a finalist for best paper at the Conference on Robot Learning. Dr Gombolay was selected as a DARPA Riser in 2018, received 1st place for the Early Career Award from the National Fire Control Symposium, and was awarded a NASA Early Career Fellowship for increasing science autonomy in space.

Programe Committee

  • Athirai A. Irissappane, University of Washington, US
  • Adrian Agogino, University of California, Santa Cruz, US
  • Raphael Avalos, Vrije Universiteit Brussel, BE
  • Wolfram Barfuss, University of Leeds, UK
  • Reinaldo Bianchi, FEI University Center, BR
  • Daan Bloembergen, Centrum Wiskunde & Informatica, NL
  • Rodrigo Bonini, Federal University of ABC, BR
  • Roland Bouffanais, University of Ottawa, CA
  • Vinicius Carvalho, University of São Paulo, BR
  • Raphael Cobe, Advanced Institute for AI, BR
  • Esther Colombini, University of Campinas, BR
  • Anna Costa, University of São Paulo, BR
  • Richard Dazeley, Deakin University, AU
  • Mathijs De Weerdt, Delft University of Technology, NL
  • Yunshu Du, Washington State University, US
  • Elias Fernández Domingos, Vrije Universiteit Brussel, BE
  • Cameron Foale, c.foale@federation.edu.au, AU
  • Julian Garcia, Monash University, AU
  • Ruben Glatt, Lawrence Livermore National Lab, US
  • Josiah Hanna, University of Edinburgh, UK
  • Brent Harrison, Georgia Institute of Technology, US
  • Daniel Hernandez, University of York, UK
  • Pablo Hernandez-Leal, Borealis AI, CA
  • Michael Kaisers, Centrum Wiskunde & Informatica, NL
  • Johan Källström, Linköping University, SE
  • Mari Kawakatsu, Princeton University, US
  • Ho-fung Leung, The Chinese University of Hong Kong, HK
  • Guangliang Li, University of Amsterdam, NL
  • Pieter Libin, Vrije Universiteit Brussel, BE
  • Kleanthis Malialis, University of Cyprus, CY
  • Karl Mason, Cardiff University, UK
  • Ramona Merhej, INESC-ID and IST, University of Lisbon, PT
  • Arno Moonens, Vrije Universiteit Brussel, BE
  • Frans Oliehoek, Delft University of Technology, NL
  • Bei Peng, University of Oxford, UK
  • Hélène Plisnier, Vrije Universiteit Brussel, BE
  • Gabriel Ramos, Universidade do Vale do Rio dos Sinos, BR
  • Giorgia Ramponi, Politecnico di Milano, IT
  • Mathieu Reymond, Vrije Universiteit Brussel, BE
  • Golden Rockefeller, Oregon State University, US
  • Francisco Santos, Universidade de Lisboa, PT
  • Yash Satsangi, Tilburg University, NL
  • Jivko Sinapov, Tufts University, US
  • Ibrahim Sobh, Valeo, EG
  • Denis Steckelmacher, Vrije Universiteit Brussel, BE
  • Paolo Turrini, University of Warwick, UK
  • Peter Vamplew, Federation University Australia, AU
  • Miguel Vasco, INESC-ID and IST, University of Lisbon, PT
  • Vitor Vasconcelos, University of Amsterdam & Princeton University, NL/US
  • Timothy Verstraeten, Vrije Universiteit Brussel, BE
  • Baoxiang Wang, The Chinese University of Hong Kong, HK
  • Marco Wiering, University of Groningen, NL
  • Connor Yates, Oregon State University, US
  • Changxi Zhu, South China University of Technology, CN
  • Luisa Zintgraf, University of Oxford, UK

Organization

This year's workshop is organised by: Senior Steering Committee Members:
  • Enda Howley (National University of Ireland Galway, IE)
  • Daniel Kudenko (University of York, UK)
  • Patrick Mannion (National University of Ireland Galway, IE)
  • Ann Nowé (Vrije Universiteit Brussel, BE)
  • Sandip Sen (University of Tulsa, US)
  • Peter Stone (University of Texas at Austin, US)
  • Matthew Taylor (Washington State University, US)
  • 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.2021 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