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<!doctype html>
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<a href="#" class="navbar-brand scroll-top logo animated bounceInLeft"><b><i>Cooperative Multi-Agent Systems Decision-making and Learning</i></b></a> </div>
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<h3>Invited Speakers</h3>
<p>Intrinsically motivated AI agent modeling in MAS</p>
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<p>Game-theoretic approaches and Cognitive Computing in Cooperative MAS</p>
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<!-- <li>
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<h3>Self-Adaptive MAS in Cooperation</h3>
<p>Efficient planning and learning adapting group members to achieve team goals</p>
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<li>
<img src="images/slides/5.png" alt="" />
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<h3>Innate-values-driven behaviors</h3>
<p>Cognitive models in swarm intelligence and robotics</p>
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<h3>AAAI 2025 Full-Day Workshop <br /> Cooperative Multi-Agent Systems Decision-making and Learning: <br />Human-Multi-Agent Cognitive Fusion
<br />Pennsylvania Convention Center, Philadelphia, PA, USA, Mar. 03, 2025
<br />AAAI CMASDL Workshop (#W11): <a href="https://aaai.org/conference/aaai/aaai-25/workshop-list/" target="blank">https://aaai.org/conference/aaai/aaai-25/workshop-list/</a>
<br />[This workshop will be held in person]
<br />[AAAI 2025 registration (workshop-only) is required]
<br />Submission Link: <a href="https://easychair.org/conferences/?conf=aaai2025cmasdlworksh" target="blank">https://easychair.org/conferences/?conf=aaai2025cmasdlworksh</a>
<!-- <br />Workshop paper submission deadline: 22 November 2024, 11:59 pm Pacific Time. -->
<br />Workshop paper submission deadline: 6 December 2024, 11:59 pm Pacific Time.
<br />Notification to authors: 25 December 2024.
<br />Camera-ready submission deadline: 20 January 2025.
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<h2>About</h2>
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<p style="font-size: 20px;">This workshop focuses on the role of decision-making and learning in human-multi-agent cooperation, viewed through the lens of cognitive modeling.
AI technologies, particularly in human-robot interaction, are increasingly focused on cognitive modeling, encompassing everything from visual processing to symbolic reasoning
and action recognition. These capabilities support human-agent cooperation in complex tasks. </p>
<p style="font-size: 20px;">
<img src="images/ERG.png" align="right" alt="" width="30%" hspace="" vspace="">
Natural agents, like humans, often make decisions based on a blend of biological, social, and cognitive motivations, as elucidated by combined motivations' model like Maslow’s Hierarchy of Needs and
Alderfer’s Existence-Relatedness-Growth (ERG) theory. On the other hand, the AI agent can be regarded as a self-organizing system that also presents various needs and motivations
in its evolution through decision-making and learning to adapt to different scenarios and satisfy their needs. Combined with AI agent capacity to aid decision-making, it opens up new horizons in human-multi-agent collaboration.
This potential is crucially essential in the context of interactions between human agents and intelligent agents, when considering to establish stable and reliable relationships in their cooperation,
particularly in adversarial and rescue mission environments. </p>
<p style="font-size: 20px;">This workshop will bring together researchers from multi-agent systems (MAS) and human-robot interaction (HRI) and aims to advance the field by exploring how cognitive science,
mathematical modeling, statistical analysis, software simulations, and hardware demonstrations can help answer critical questions about decision-making and learning in these cooperative environments.
The goal is to understand better how cognitive modeling can enhance cooperation between humans and AI agents, especially in complex, high-stakes scenarios.</p>
<div class="row feature design">
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<h3>Topics</h3>
<p style="font-size: 20px;">We solicit contributions from topics including but not limited to:</p>
<ul>
<!-- <li style="font-size: 18px;">MAS cognitive modeling</li> -->
<li style="font-size: 18px;">Human-multi-agent cognitive modeling</li>
<li style="font-size: 18px;">Human-multi-agent trust networks</li>
<li style="font-size: 18px;">Trustworthy AI agents in Human-robot interaction</li>
<li style="font-size: 18px;">Trust based Human-MAS decision-making and learning</li>
<li style="font-size: 18px;">Consensus in Human-MAS collaboration</li>
<!-- <li style="font-size: 18px;">Human-multi-agent reinforcement learning modeling</li> -->
<li style="font-size: 18px;">Intrinsically motivated AI agent modeling in Human-MAS</li>
<li style="font-size: 18px;">Innate-values-driven reinforcement learning</li>
<!-- <li style="font-size: 18px;">MAS deep reinforcement learning</li> -->
<li style="font-size: 18px;">Multi-Objective MAS decision-making and learning</li>
<li style="font-size: 18px;">Adaptive learning with social rewards</li>
<li style="font-size: 18px;">Cognitive models in swarm intelligence and robotics</li>
<li style="font-size: 18px;">Game-theoretic approaches in MAS decision-making</li>
<li style="font-size: 18px;">Cognitive model application in intelligent social systems</li>
</ul>
</div>
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<h2 style="color: white;">Invited Speakers</h2>
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<!-- item -->
<div class="col-md-3 col-sm-6 col-xs-6 text-center">
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<!-- Image Hover Block -->
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<!-- Image -->
<img class="img-responsive" src="images/ps.png" alt="" /> </div>
<!-- Member Details -->
<h3><a href="https://www.cs.utexas.edu/~pstone/" target="blank">Peter Stone</a></h3>
<!-- Designation -->
<p>AAAI/IEEE/ACM Fellow, <br />University of Texas at Austin</p>
</div>
</div>
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<h3><a href="https://ae.gatech.edu/directory/person/panagiotis-tsiotras" target="blank">Panagiotis Tsiotras</a></h3>
<!-- Designation -->
<p>Fellow of the AIAA, IEEE, and AAS,<br />Georgia Institute of Technology</p>
</div>
</div>
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<img class="img-responsive" src="images/ks.png" alt="" /> </div>
<!-- Member Details -->
<h3><a href="https://www.ri.cmu.edu/ri-faculty/katia-sycara/" target="blank">Katia Sycara</a></h3>
<!-- Designation -->
<p>Fellow of the AAAI and IEEE,<br />Carnegie Mellon University</p>
</div>
</div>
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<img class="img-responsive" src="images/bm.jpg" alt="" /> </div>
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<h3><a href="https://bmutlu.github.io/" target="blank">Bilge Mutlu</a></h3>
<!-- Designation -->
<p>Director of the People and Robots Laboratory, <br />University of Wisconsin–Madison</p>
</div>
</div>
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<img class="img-responsive" src="images/kp.png" alt="" /> </div>
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<h3><a href="https://web.eecs.umich.edu/~kuipers/" target="blank">Benjamin Kuipers</a></h3>
<!-- Designation -->
<p>Fellow of AAAI, IEEE, and AAAS <br />University of Michigan</p>
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<h3><a href="https://drmatttaylor.net/" target="blank">Matthew E. Taylor</a></h3>
<!-- Designation -->
<p>Fellow of Amii and CIFAR AI Chair,<br />University of Alberta</p>
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<img class="img-responsive" src="images/sk.png" alt="" /> </div>
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<h3><a href="https://idm-lab.org/" target="blank">Sven Koenig</a></h3>
<!-- Designation -->
<p>AAAI Fellow, the Association of ACM/IEEE/AAAS <br />University of California, Irvine</p>
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<img class="img-responsive" src="images/mg.png" alt="" /> </div>
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<h3><a href="https://www-users.cse.umn.edu/~gini/" target="blank">Maria Gini</a></h3>
<!-- Designation -->
<p>EIF RAS, IEEE Fellow,<br />University of Minnesota</p>
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<h4>Workshop Video: Part 1 (9:00 am -- 12:30 pm)</h4>
<div class="iframe_container"><iframe class="video" width="560" height="315" src="https://www.youtube.com/embed/-pVmsgo-RNU?si=4C8zLdZYwkhgirZs"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe></div> -->
<!-- <p style="font-size: 18px;">This workshop is part of <a href="https://aaai.org/aaai-conference/aaai-24-workshop-list/#ws13" target="blank">the Thirty-Eight AAAI Conference on Artificial Intelligence (AAAI-24)</a>.
Virtual attendance will be available to every audience who has registered for the workshop. </p> -->
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<h4>Workshop Video: Part 2 (2:00 pm -- 17:30 pm)</h4>
<div class="iframe_container"><iframe class="video" width="560" height="315" src="https://www.youtube.com/embed/OZmbGpl2ypw?si=cuj-6hez_Bm5areB"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe></div> -->
<!-- <p style="font-size: 18px;">Submission Link: <a href="https://easychair.org/conferences/?conf=aaai2024cmasdlworksh" target="blank">https://easychair.org/conferences/?conf=aaai2024cmasdlworksh</a></p>
<ul class="listArrow">
<li style="color:#0000FF; font-size: 18px;">Submission Deadline: 30 Nov. 2023</li>
<li style="color:#0000FF; font-size: 18px;">Author Notification Date: 16 Dec. 2023</li>
<li style="color:#0000FF; font-size: 18px;">Camera Ready Date: 15 Jan. 2024</li>
<li style="color:#FF0000; font-size: 18px;">Workshop Date: 26 Feb. 2024</li>
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<h4>Publication Opportunities:</h4>
<p style="font-size: 18px;">
Acceptance papers will be made publicly available on the workshop website.
These non-archival papers and their corresponding posters will also remain available on this website after the workshop. The authors will retain copyright of their papers.
Selected high-quality papers from the workshop will provide the opportunity to adapt or expand into a book chapter publishing in <a href="https://www.nowpublishers.com" target="blank">NowPublishers</a> according to authors' needs and agreement.
</p>
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</section>
<section id="plans" class="page-section">
<div class="container">
<div class="heading text-center">
<!-- Heading -->
<h2>Workshop Schedule</h2>
<!-- <p style="font-size: 20px;">TBD </p>
</div> -->
<div class="row mt-0">
<font size="4" face="Helvetica">
<table width="100%">
<tbody>
<tr>
<td class="left-col" style="color:navy"></td> <br>
<td style="color:maroon">All times in EST (GMT-5), Mar. 03, 2025</td>
</tr>
<tr>
<td class="left-col" style="color:navy">9:00a</td> <br>
<td style="color:navy">Welcome Remarks</td>
</tr>
<tr>
<td class="left-col" style="color:navy">9:10a</td>
<td style="color:navy">Invited Session 1</td>
</tr>
<tr>
<td style="color:maroon">9:10a-9:40a</td>
<td>
<div class="collapsible">Peter Stone (University of Texas at Austin) - Topic: Advances in Ad Hoc Teamwork: Multiagent Collaboration without Pre-Coordination</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
As autonomous agents proliferate in the real world, both in software and robotic settings, they will increasingly need to band together for
cooperative activities with previously unfamiliar teammates. In such "ad hoc" team settings, team strategies cannot be developed a priori.
Rather, an agent must learn to cooperate with many types of teammates: it must collaborate without pre-coordination. This talk will
summarize research along two recent directions of ad hoc teamwork. First, it will consider how to generate diverse teammates for the
purpose of training a robust ad hoc teamwork capability. Second, it will introduce a new generalization of ad hoc teamwork, called N-agent
ad hoc teamwork, in which a collection of autonomous agents must interact and cooperate as a subteam with dynamically varying numbers and types of teammates.
</p>
</div>
</td>
</tr>
<tr>
<td style="color:maroon">9:40a-10:10a</td>
<td>
<div class="collapsible">Katia Sycara (Carnegie Mellon University) - Topic: Modeling Trust in Human-Swarm Collaboration</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
Robotic swarms consist of simple, typically homogeneous robots that interact with other robots and the environment. Swarm robots are coordinated via simple control
laws to generate emergent behaviors. Owing to their scalability and natural robustness to individual robot failures, swarms are attractive for large-scale applications
in unstructured and dynamic environments. Human presence is important in swarm applications since humans can recognize and mitigate shortcomings of the swarm, such as
limited sensing and communication. Also, humans can provide new goals to the swarm as the environment and mission requirements dictate. In such interactions, correct trust
calibration to the swarm is crucial for task performance. By correctly trusting the swarm, the operator can leave the swarm undisturbed to perform the task at hand. On the
other hand, if swarm performance degrades, the operator can provide appropriate corrective interventions. In this talk I will discuss the challenges in formulating human-swarm
interaction models and provide examples for different scenarios. Additionally, I will present empirical results from human experiments and discuss open problems and research directions.
</p>
</div>
</td>
</tr>
<tr>
<td style="color:green">10:10a</td>
<td>
<div class="collapsible" style="color:green">Contributed Papers - Oral Presentations</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<font style="color:navy">10:10a-10:22a</font> --- Innate-Values-driven Reinforcement Learning <br>
<font style="color:navy">10:22a-10:34a</font> --- What are you saying? Towards explaining communication in MARL <br>
<font style="color:navy">10:34a-10:46a</font> --- Incentive Design in Hedonic Games with Permission Structures <br>
<font style="color:navy">10:46a-10:58a</font> --- Detecting Adversarial Interference in Cooperative Tasks Through Multi-dimensional Bayesian Trust Metric <br>
<font style="color:navy">10:58a-11:10a</font> --- Towards Multi-Agent Spatio-Temporal Area Restoration <br>
</p>
</div>
</td>
</tr>
<tr>
<td>11:10a-11:25a</td>
<td>Coffee Break</td>
</tr>
<tr>
<td style="color:navy">11:25a</td>
<td style="color:navy">Invited Session 2</td>
</tr>
<tr>
<td style="color:maroon">11:25a-11:55a</td>
<td>
<div class="collapsible">Benjamin Kuipers (University of Michigan) - Topic: Trust Is a Utility</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
Trust, or more precisely, a reputation for trustworthiness, is a capital asset for an individual decision-maker. One benefit to an individual of trust from others is
increased opportunity to participate in cooperative, positive-sum, activities with those others. By explicitly considering trust, each individual decision takes place
in the context of a society, with many and varied decision-makers and many and varied decisions to make. Actions that deplete that asset decrease the individual’s ability
to cooperate, their ability to respond to threats and opportunities, and perhaps even their survival. Since trust is so important to the decision-maker, it must be included
in the utility measure. The value of an action selected by a utility-maximization algorithm depends critically on how the utility measure is defined. In the classic Prisoner’s
Dilemma, the utility-maximization algorithm selects a shockingly bad action. The proper conclusion is that the algorithm was applied to an incorrect model of the decision situation,
in this case by omitting trust from the utility measure. A second benefit of trust to a decision-maker is to simplify the model of the world, making planning more tractable by reducing
the need for contingency planning. One trusts that cooperative partners will act in certain reliable and benevolent ways, and that certain unlikely results of actions will not occur.
Of course, assumptions like these are not always true. People do sometimes break their promises, run red lights, or fail to repay their debts. Trust has substantial value, but is also
vulnerable to exploitation. How should intelligent agents, human and artificial, behave and reason to maximize gains from trust-based cooperation and minimize losses from trust failures?
In order to survive and thrive through cooperation, our human society has evolved a variety of methods for estimating the trustworthiness of potential partners, and for anticipating and
responding to trust failures. Our goal is to identify, formalize, and implement these methods to encourage trust and cooperation in a society including artificially intelligent agents
as well as human beings.
</p>
</div>
</td>
</tr>
<tr>
<td style="color:maroon">11:55a-12:25p</td>
<td>
<div class="collapsible">Panagiotis Tsiotras (Georgia Institute of Technology) - Topic: Training Multi-Agent Reinforcement Learning Games with Mean Field Interactions</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
State-of-the-art multi-agent reinforcement learning (MARL) algorithms such as MADDPG and MAAC fail to scale in situations where the number of agents becomes large.
Mean-field theory has shown encouraging results in modeling large-scale agent behavior in large teams through a continuum approximation of the agent population and
its interaction with the environment. Although MF approximations have been used extensively in MF control problems, the fully adversarial case has received less attention.
In this talk, we will investigate the behaviors of two large population teams competing in a discrete environment, The team-level interactions are modeled as a zero-sum game,
while the dynamics within each team are formulated as a collaborative mean-field team problem. Following the mean-field literature, we first approximate the large-population
team game with its infinite-population limit. By introducing two fictitious coordinators we transform the infinite-population game into an equivalent zero-sum coordinator game.
We study the optimal strategies for each team for the infinite population limit via a novel reachability analysis, and we show that the obtained team strategies are decentralized
and are ε-optimal for the original finite-population game. This allows the training of competing teams with hundreds or thousands of agents. We will demonstrate the application
of this theoretical result to agent training in large-population multi-agent reinforcement learning (MARL) problems.
</p>
</div>
</td>
</tr>
<tr>
<td>12:25p-1:20p</td>
<td>Lunch Break</td>
</tr>
<!-- <tr>
<td style="color:navy">12:20p</td>
<td style="color:navy">Break</td>
</tr> -->
<tr>
<td style="color:green">1:20p</td>
<td>
<div class="collapsible" style="color:green">Contributed Papers - Poster Session I</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<font style="color:navy">2:00p-2:05p</font> --- Hierarchical and Heterogeneous MARL for Coordinated Multi-Robot Box-Pushing Environment <br>
<font style="color:navy">2:05p-2:10p</font> --- Digital Twin Synchronization for Real-Time Process Control Using Sim-RL in Smart Additive Manufacturing <br>
<font style="color:navy">2:10p-2:15p</font> --- On the Decision-Making Abilities in Role-Playing using Large Language Models <br>
<font style="color:navy">2:15p-2:20p</font> --- Enabling Rapid Shared Human-AI Mental Model Alignment via the After-Action Review <br>
<font style="color:navy">2:20p-2:25p</font> --- Recovering Reward Functions from Distributed Expert Demonstrations with Maximum-likelihood <br>
<font style="color:navy">2:25-2:40p</font> --- Q/A
</p>
</div>
</td>
</tr>
<tr>
<td style="color:navy">2:00p</td>
<td style="color:navy">Invited Session 3</td>
</tr>
<tr>
<td style="color:maroon">2:00p-2:30p</td>
<td>
<div class="collapsible">Sven Koenig (University of California, Irvine) - Topic: Multi-Robot Systems: Ant Robots and Auction Robots</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
Ant robots have the advantage that they are easy to program and cheap to build. We study ant robots for the one-time or repeated coverage of terrain. They cannot use conventional
planning methods due to their limited sensing and computational capabilities. To overcome these limitations, we study navigation methods that leave markings in the terrain,
similar to what real ants do. These markings are shared among all ant robots and allow them to cover terrain even if they do not have any kind of memory, cannot maintain
maps of the terrain, nor plan complete paths. Our navigation methods do not require the ant robots to be localized. They can be used by single ant robots and groups of ant
robots and are robust even if the ant robots are moved without realizing this (say, by people running into them), some ant robots fail, and some markings get destroyed.
Auction robots have to repeatedly reassign tasks among themselves, for example, as they learn more about the terrain, as robots fail, or as additional tasks get introduced.
We study auction-based robot coordination systems that assign locations to robots (that then need to visit these locations) and are based on sequential single-item auctions.
They can - perhaps surprisingly - provide constant-factor quality guarantees in some cases even though they run in polynomial time and, more generally, combine the advantageous
properties of parallel and combinatorial auctions. For a given number of additional locations to be assigned during each round, the communication and winner determination times
do not depend on the number of locations, which helps them to scale up to many locations for small bundle sizes.
</p>
</div>
</td>
</tr>
<tr>
<td style="color:maroon">2:30-3:00p</td>
<!-- <td>
<div class="collapsible">Christopher Amato (Northeastern University) - Topic: Correcting Some Misconceptions about MARL</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
Multi-agent reinforcement learning (MARL) has exploded in popularity but there is a lack of understanding of when current methods work and what is the best way to learn in multi-agent settings.
This talk will include some of the fundamental challenges and misunderstandings of multi-agent reinforcement learning. In particular, it will discuss how 1) centralized critics are not strictly
better than decentralized critics in MARL (and can be worse), and 2) state-based critics are unsound and work well only in fully-observable multi-agent problems. Furthermore, it will discuss
related methods in value-based MARL.
</p>
</div>
</td> -->
</tr>
<!-- <tr>
<td style="color:navy">3:35p</td>
<td style="color:navy">Coffee Break</td>
</tr> -->
<tr>
<td style="color:green">3:00p</td>
<td>
<div class="collapsible" style="color:green">Contributed Papers - Poster Session II</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<font style="color:navy">3:00p-3:05p</font> --- Training Adaptive Foraging Behavior for Robot Swarms Using Distributed Neuroevolution of Augmented Topologies <br>
<font style="color:navy">3:05p-3:10p</font> --- “I apologize for my actions”: Emergent Properties and Technical Challenges of Generative Agents <br>
<font style="color:navy">3:10p-3:15p</font> --- MASR: Multi-Agent System with Reflection for the Abstraction and Reasoning Corpus <br>
<font style="color:navy">3:15p-3:20p</font> --- Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support <br>
<font style="color:navy">3:20p-3:30p</font> --- Q/A <br>
</p>
</div>
</td>
</tr>
<tr>
<td>3:30p-3:45p</td>
<td>Coffee Break</td>
</tr>
<tr>
<td style="color:navy">3:45p</td>
<td style="color:navy">Invited Session 4</td>
</tr>
<tr>
<td style="color:maroon">3:45p-4:15p</td>
<!-- <td>
<div class="collapsible">Marco Pavone (Stanford University) - Topic: Artificial Currency Based Government Welfare Programs: From Optimal Design to Audit Games</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
Artificial currencies have grown in prominence in many real-world resource allocation settings, helping alleviate fairness and equity concerns of traditional monetary mechanisms
that often favor users with higher incomes. In particular, artificial currencies have gained traction in government welfare programs that support eligible users in the population,
e.g., transit benefits programs provide eligible users with subsidized public transit. While such artificial currency based welfare programs are typically well-intentioned and offer
immense potential in improving the outcomes for the eligible group of users, the deployment of many such programs in practice is nascent; hence, the efficacy and optimal design of such
programs still needs to be formalized. Moreover, such programs are susceptible to several fraud mechanisms, with a notable concern being misreporting fraud, wherein users can misreport
their private attributes to gain access to more artificial currency (credits) than they are entitled to.
This talk introduces models and methods to study the equilibrium outcomes and the optimal design of such artificial currency based welfare programs to achieve particular societal objectives
of an administrator running the benefits program. Moreover, to address the issue of misreporting fraud, we propose a natural audit game, wherein the administrator can audit users at some cost
and levy fines against them for misreporting information. Methodologically, we propose a bi-level optimization framework to optimally design artificial currency based welfare mechanisms and
develop convex and linear programming approaches to compute the associated equilibrium outcomes. Finally, to highlight the practical viability of our proposed methods, we present case studies
in the context of two welfare programs: (i) San Mateo County’s Community Transport Benefits Program, wherein users are provided with travel credits to offset some of their payments for using
tolled express lanes on highways, and (ii) Washington D.C.’s federal transit benefits programs that provide subsidized public transit to federal employees.
</p>
</div>
</td> -->
</tr>
<tr>
<td style="color:maroon">4:15p-4:45p</td>
<!-- <td>
<div class="collapsible">Kevin Leyton-Brown (UBC) - Topic: Modeling Nonstrategic Human Play in Games</div>
<div class="hidden-content">
<p style="margin-top:8pt; margin-bottom:7pt; text-align:justify">
<b>
<font size="">Abstract:</font>
</b>
It is common to assume that players in a game will adopt Nash equilibrium strategies.
However, experimental studies have demonstrated that Nash equilibrium is often a poor description of human players' behavior,
even in unrepeated normal-form games. Nevertheless, human behavior in such settings is far from random. Drawing on data from real human play,
the field of behavioral game theory has developed a variety of models that aim to capture these patterns.
This talk will survey over a decade of work on this topic, built around the core idea of treating behavioral game theory as a machine learning problem.
It will touch on questions such as:
- Which human biases are most important to model in single-shot game theoretic settings?
- What loss function should be used to evaluate and fit behavioral models?
- What can be learned about examining the parameters of these models?
- How can richer models of nonstrategic play be leveraged to improve models of strategic agents?
- When does a description of nonstrategic behavior "cross the line" and deserve to be called strategic?
- How can advances in deep learning be used to yield stronger--albeit harder to interpret--models?
Finally, there has been much recent excitement about large language models such as GPT-4.
Time permitting, the talk will conclude by describing how the economic rationality of such models can be assessed and
presenting some initial experimental findings showing the extent to which these models replicate human-like cognitive biases.
</p>
</div>
</td> -->
</tr>
<!-- <tr>
<td>5:10-5:15p</td>
<td>Q/A and Discussion for Invited Session 4</td>
</tr> -->
<tr>
<td style="color:navy">4:45p-5:35p</td>
<td style="color:navy">Expert Panel Discussion</td>
</tr>
<tr>
<td style="color:navy">5:35p-5:40p</td>
<td style="color:navy">Concluding Remarks</td>
</tr>
</tbody>
</table>
</font>
</div>
</div>
</section>
<section id="work" class="page-section page">
<div class="container text-center">
<div class="heading">
<h2>Contributions</h2>
</div>
<h3 style="color:yellow"> Oral Presentation</h3>
<!-- <p style="font-size: 20px;">TBD </p>
-->
<font size="4" face="Helvetica">
<br>
<table width="100%">
<tbody>
<tr>
<td>[#1]</td>
<td style="color:aqua" align="left">Innate-Values-driven Reinforcement Learning, <br> <em style="color:thistle" align="left">Qin Yang</em><br>
<a href="https://drive.google.com/file/d/1YmV350cnOeOEyOGILxesv-m-Y_jBbGj8/view?usp=drive_link" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/16eJ3uckH0CTe1_wDslcmG5dgQNg533TO/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#2]</td>
<td style="color:aqua" align="left">What are you saying? Towards explaining communication in MARL, <br> <em style="color:thistle" align="left">Cristian Morasso, Daniele Meli and Alessandro Farinelli</em><br>
<a href="https://drive.google.com/file/d/1mFCiui6N1OJIRvYg-97wSMtxnsB5Bs8A/view?usp=drive_link" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1xPQ_FvmnEnrGEW1moqBkIGEQQwBW6vOE/view?usp=drive_link" target="_blank"> [Link to Poster]</a>
</td>
</td>
</tr>
<tr>
<td>[#6]</td>
<td style="color:aqua" align="left">Incentive Design in Hedonic Games with Permission Structures, <br> <em style="color:thistle" align="left">Yuta Akahoshi, Yao Zhang, Kei Kimura, Taiki Todo and Makoto Yokoo</em><br>
<a href="https://drive.google.com/file/d/1PqUPpcCbCbPLKz2SSdCIfhKY2haPihI7/view?usp=sharing" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1DIblagl5PWtQrXUZMVbNwz4IZ14GBjM1/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</td>
</tr>
<tr>
<td>[#15]</td>
<td style="color:aqua" align="left">Detecting Adversarial Interference in Cooperative Tasks Through Multi-dimensional Bayesian Trust Metric, <br> <em style="color:thistle" align="left">Angel Sylvester and Maria Gini</em><br>
<a href="https://drive.google.com/file/d/16WdaaYzrDEJpxWW80kfGknVXxeDmt9fd/view?usp=drive_link" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1X48jpw0L82aMQVqTnZZqPbPBoHlgnCiT/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#16]</td>
<td style="color:aqua" align="left">Towards Multi-Agent Spatio-Temporal Area Restoration, <br> <em style="color:thistle" align="left">Amel Nestor Docena and Alberto Quattrini Li </em><br>
<a href="https://drive.google.com/file/d/1twRvztFjjAzKwo598dlZBcllmglbi4Cm/view?usp=drive_link" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1tEnxwbGL0Hg8TWPbeIs9jmdM2RG3pidI/view?usp=drive_link" target="_blank"> [Link to Poster]</a>
</td>
</tr>
</tbody>
</table>
</font>
<br>
<h3 style="color:yellow"> Posters</h3>
<!-- <p style="font-size: 20px;">TBD </p> -->
<font size="4" face="Helvetica">
<br>
<table width="100%">
<tbody>
<tr>
<td>[#17]</td>
<td style="color:aqua" align="left">Hierarchical and Heterogeneous MARL for Coordinated Multi-Robot Box-Pushing Environment, <br> <em style="color:thistle" align="left">Ebasa Temesgen, Sarah Boelter and Maria Gini</em><br>
<a href="https://drive.google.com/file/d/1m64TaYEUFTlu5qTFmEuRjJOIPCAYVLtG/view?usp=drive_link" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/15UmMQxKjywLRoPMj94dLuzDUDhb98GQA/view?usp=drive_link" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#8]</td>
<td style="color:aqua" align="left">Digital Twin Synchronization for Real-Time Process Control Using Sim-RL in Smart Additive Manufacturing, <br> <em style="color:thistle" align="left"> Matsive Ali, Sandesh Giri, Sen Liu and Qin Yang</em><br>
<a href="https://drive.google.com/file/d/1Tj1OD_OnKc6zziKwLNheGecEQj-CZzsw/view?usp=sharing" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1mH1n9wFar-3eKUjyNtTX9XkARcEkqrm8/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#10]</td>
<td style="color:aqua" align="left">On the Decision-Making Abilities in Role-Playing using Large Language Models, <br> <em style="color:thistle" align="left">Chenglei Shen, Guofu Xie, Xiao Zhang and Jun Xu</em><br>
<a href="https://drive.google.com/file/d/1pNfF1M53YVv95pet2Qf6SP6Oy4Svk389/view?usp=sharing" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1ceLG-agYxLuEqFdysymRYRW261NfI5uF/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td> [#11]</td>
<td style="color:aqua" align="left">Enabling Rapid Shared Human-AI Mental Model Alignment via the After-Action Review, <br> <em style="color:thistle" align="left">Edward Gu, Hosea Siu, Melanie Platt, Isabelle Hurley, Jaime Peña and Rohan Paleja</em><br>
<a href="https://drive.google.com/file/d/1CBCPawl5FlMcgkynvVgMSh0foOKayJyI/view?usp=sharing" target="_blank"> [Link to Paper]</a>
<!-- <a href="https://drive.google.com/file/d/19pM6t3aR05ZGcK9vehmyZAA2PLLl8eBj/view?usp=sharing" target="_blank"> [Link to Poster]</a> -->
</td>
</tr>
<tr>
<td>[#13]</td>
<td style="color:aqua" align="left">Recovering Reward Functions from Distributed Expert Demonstrations with Maximum-likelihood, <br> <em style="color:thistle" align="left">Guangyu Jiang, Mahdi Imani, Nathaniel Bastian and Tian Lan</em><br>
<a href="https://drive.google.com/file/d/1kvqT2LtPO9fW-1lwaJD6-gWqqPg-Nvb3/view?usp=sharing" target="_blank"> [Link to Paper]</a>
<a href="https://drive.google.com/file/d/1JiIwwRchXT-WmnW2PaS3Q94xizulc1pi/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#18]</td>
<td style="color:aqua" align="left">Training Adaptive Foraging Behavior for Robot Swarms Using Distributed Neuroevolution of Augmented Topologies, <br> <em style="color:thistle" align="left">Tameem Uz Zaman, Pigar Biteng, Eric Rodriguez and Qi Lu</em><br>
<a href="https://drive.google.com/file/d/1bDEvyyeoSlofx4j38e_MfDhX4jVSmqbk/view?usp=sharing" target="_blank"> [Link to Paper]</a>
<a href="https://drive.google.com/file/d/11CB_vWM2RVbA2SlVbbLPHvbOd6ui79RE/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#4]</td>
<td style="color:aqua" align="left">“I apologize for my actions”: Emergent Properties and Technical Challenges of Generative Agents, <br> <em style="color:thistle" align="left">N'Yoma Diamond and Soumya Banerjee</em><br>
<a href="https://drive.google.com/file/d/1N_3JSi0ucK4fztCcSI-IXk-r-QACSRjp/view?usp=sharing" target="_blank"> [Link to Paper]</a>
<!-- <a href="https://drive.google.com/file/d/1AIwHntI4YlnuMYClWXi4e0NLP-uNoRc5/view?usp=sharing" target="_blank"> [Link to Poster]</a> -->
</td>
</tr>
<tr>
<td>[#5]</td>
<td style="color:aqua" align="left">MASR: Multi-Agent System with Reflection for the Abstraction and Reasoning Corpus, <br> <em style="color:thistle" align="left">Kiril Bikov, Mikel Bober-Irizar and Soumya Banerjee</em><br>
<a href="https://drive.google.com/file/d/1Lbk49iNg_l13TAEeIgFxjcJsIlxmnzlg/view?usp=sharing" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1qSxz59bk6tjOG1qKnDLYDo-ha7HKBM1l/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr>
<tr>
<td>[#9]</td>
<td style="color:aqua" align="left">Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support, <br> <em style="color:thistle" align="left">Anubha Mahajan, Shreya Hegde, Ethan Shay, Daniel Wu and Aviva Prins</em><br>
<a href="https://drive.google.com/file/d/13o0xJWDNaMAG3ZvG7XEs7awKzAQaLz0J/view?usp=sharing" target="_blank"> [Link to Paper]</a>
<!-- <a href="https://drive.google.com/file/d/1YjPziGMHqBPOjsP9r7jngiSvG18CJJxc/view?usp=sharing" target="_blank"> [Link to Poster]</a> -->
</td>
</tr>
<!-- <tr>
<td>[#3]</td>
<td style="color:aqua" align="left">Reasoning- and Learning-Based Language Generation for Cognitive Agents, <br> <em style="color:thistle" align="left">Marjorie McShane, Sergei Nirenburg, Sanjay Oruganti, Michael Roberts and Jesse English</em><br>
<a href="https://drive.google.com/file/d/1pEQ3pr0rpW1SKtDjiejWgPtyS4gGbTMu/view?usp=sharing" target="_blank"> [Link to Paper]</a><a href="https://drive.google.com/file/d/1YjPziGMHqBPOjsP9r7jngiSvG18CJJxc/view?usp=sharing" target="_blank"> [Link to Poster]</a>
</td>
</tr> -->
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<p style="font-size: 20px;" align="left">Submissions can contain relevant work in all possible stages, including those recently published work, is under submission elsewhere,
was only recently finished, or is still ongoing. Authors of papers published or under submission elsewhere are encouraged to submit these papers or short versions (including abstracts)
to the workshop, educating other researchers about their work, as long as resubmissions are clearly labeled to avoid copyright violations.</p>
<p style="font-size: 20px;" align="left">We welcome contributions of both short (2-4 pages) and long papers (6-8 pages) related to our stated vision in the <a href="https://aaai.org/conference/aaai/aaai-25/workshops-call/" target="blank">AAAI 2025 proceedings format</a>.
Position papers and surveys are also welcome. The contributions will be non-archival but will be hosted on our workshop website. All contributions will be peer reviewed (single-blind). </p>
<p style="font-size: 20px;" align="left">Workshop paper submission deadline: 6 December 2024, 11:59 pm Pacific Time.</p>
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<p style="font-size: 20px;" align="left">Contributions are to be submitted to <a href="https://easychair.org/conferences/?conf=aaai2025cmasdlworksh" target="blank">https://easychair.org/conferences/?conf=aaai2025cmasdlworksh</a>.</p>
<p style="font-size: 20px;" align="left">Acceptance papers will be made publicly available on the workshop website. These non-archival papers and their corresponding posters will also remain available on this website after the workshop. The authors will retain copyright of their papers.</p>
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in <a href="https://aaai.org/aaai-24-conference/aaai-24-workshop-program/" target="blank">AAAI 2024 #W13</a>.</p>
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