New Zealand


Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. Modern MARL methods have focused on finding factorized value functions. While successful, the resulting methods have convoluted network structures. We take a radically different approach and build on the structure of independent Q-learners. Our algorithm LAN leverages a dueling architecture to represent decentralized policies as separate individual advantage functions w.r.t. a centralized critic that is cast aside after training. The critic works as a stabilizer that coordinates the learning and to formulate DQN targets. This enables LAN to keep the number of parameters of its centralized network independent in the number of agents, without imposing additional constraints like monotonic value functions. 
When evaluated on the SMAC, LAN shows SOTA performance overall and scores more than 80% wins in two super-hard maps where even QPLEX does not obtain almost any wins. Moreover, when the number of agents becomes large, LAN uses significantly fewer parameters than QPLEX or even QMIX. We thus show that LAN&#39;s structure forms a key improvement that helps MARL methods remain scalable.

AAMAS 2022

Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning

decpomdps

centralized training decentralized execution

cooperation

coordination

multi-agent

reinforcement learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020


Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. Modern MARL methods have focused on finding factorized value functions. While successful, the resulting methods have convoluted network structures. We take a radically different approach and build on the structure of independent Q-learners. Our algorithm LAN leverages a dueling architecture to represent decentralized policies as separate individual advantage functions w.r.t. a centralized critic that is cast aside after training. The critic works as a stabilizer that coordinates the learning and to formulate DQN targets. This enables LAN to keep the number of parameters of its centralized network independent in the number of agents, without imposing additional constraints like monotonic value functions. 
When evaluated on the SMAC, LAN shows SOTA performance overall and scores more than 80% wins in two super-hard maps where even QPLEX does not obtain almost any wins. Moreover, when the number of agents becomes large, LAN uses significantly fewer parameters than QPLEX or even QMIX. We thus show that LAN's structure forms a key improvement that helps MARL methods remain scalable.

poster

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).


Following Zhang and Grossi~(AAAI 2021), we study in more depth a variant of weighted voting games in which agents' weights are induced by a transitive support structure. 
This class of simple games is notably well suited to study the relative importance of agents in the liquid democracy framework. We first propose a pseudo-polynomial time algorithm to compute the Banzhaf and Shapley-Shubik indices for this class of game. Then, we study a bribery problem, in which one tries to maximize/minimize the voting power/weight of a given agent by changing the support structure under a budget constraint. We show that these problems are computationally hard and provide several parameterized complexity results.

Computation and Bribery of Voting Power in Delegative Simple Games


We study wisdom-of-the-crowd effects in liquid democracy on networks where agents are allowed to apportion parts of their voting weight to different proxies. We show that in this setting---unlike in the standard one where voting weight is delegated in full to only one proxy---it becomes possible to construct delegation structures that optimize the truth-tracking ability of the group. Focusing on group accuracy we contrast this centralized solution with the setting in which agents are free to choose their weighted delegations by greedily trying to maximize their own individual accuracy. While equilibria with weighted delegations may be as bad as in the standard delegations setting, they are never worse and may sometimes be better. To gain further insights into this decentralized model we experimentally study quantal response delegation strategies on random networks.

Tracking Truth by Weighting Proxies in Liquid Democracy


We consider the problem of finding a compromise between the opinions of a group of individuals on a number of mutually independent, binary topics.
In this paper, we quantify the loss in representativeness that results from requiring the outcome to have majority support, in other words, the ``price of majority support''.
Each individual is assumed to support an outcome if they agree with the outcome on at least as many topics as they disagree on.
Our results can also be seen as quantifying Anscombes paradox which states that topic-wise majority outcome may not be supported by a majority.
To measure the representativeness of an outcome, we consider two metrics.
First, we look for an outcome that agrees with a majority on as many topics as possible.
We prove that the maximum number such that there is guaranteed to exist an outcome that agrees with a majority on this number of topics and has majority support, equals $\ceil{(t+1)/2}$ where $t$ is the total number of topics.
Second, we count the number of times a voter opinion on a topic matches the outcome on that topic. The goal is to find the outcome with majority support with the largest number of matches.
We consider the ratio between this number and the number of matches of the overall best outcome which may not have majority support.
We try to find the maximum ratio such that an outcome with majority support and this ratio of matches compared to the overall best is guaranteed to exist.
For 3 topics, we show this ratio to be $5/6\approx 0.83$.
In general, we prove an upper bound that comes arbitrarily close to $2\sqrt{6}-4\approx 0.90$ when $t$ is large.
Furthermore, we numerically compute a better upper and a non-matching lower bound in the relevant range for $t$.

The Price of Majority Support


Group decisions are often complicated by a deadline. For example, in committee hiring decisions, the deadline might be a budget's start date or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one---an option that may cost dearly. Hence, committee members might prefer to agree on a reasonable, rather than the best, candidate, to avoid unfilled positions. Here, we propose a model for the above scenario - Consensus Under a Deadline (CUD)-based on a time-bounded iterative voting process. We provide theoretical convergence guarantees and an analysis of the resulting decision quality. An extensive experimental study demonstrates more subtle features of CUDs, e.g., the difference between two simple types of committee member behavior, lazy vs. proactive voters. Finally, a user study examines the differences between the behavior of rational voting bots and human voters, concluding that it may often be best to have bots play on the humans' behalf.

Reaching Consensus Under a Deadline


Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture properties (phase, category, and semantics) can be predicted from speech text and/or audio using contemporary deep learning. In extensive experiments, we show that gesture properties related to gesture meaning (semantics and category) are predictable from text features (time-aligned FastText embeddings) alone, but not from prosodic audio features, while rhythm-related gesture properties (phase) on the other hand can be predicted from audio features better than from text. These results are encouraging as they indicate that it is possible to equip an embodied agent with content-wise meaningful co-speech gestures using a machine-learning model.

Multimodal analysis of the predictability of hand-gesture properties


Autonomous agents operating in the real world often need to interact with other agents to accomplish their tasks. For such agents, the ability to model behavior of other agents – both human and artificial – without complete knowledge of their decision factors is essential. Towards realizing this ability, we present Factorial Agent Markov Model (FAMM), a model to represent behavior of other agents performing sequential tasks. In contrast with most existing models, FAMM allows for behavior of other agents to depend on multiple, time-varying latent decision factors and does not assume rationality. To enable learning of FAMM parameters by observing behavior of other agents, we provide a set of variational inference algorithms for the unsupervised, semi-supervised, and supervised settings. These Bayesian learning algorithms for the FAMM enable agents to model other agents using execution traces and domain-specific priors. We demonstrate the utility of FAMM and corresponding learning algorithms using three synthetic domains and benchmark them against existing algorithms for modeling agent behavior. Our numerical experiments demonstrate that, despite the presence of multiple time-varying latent states, our approach is capable of learning predictive models of other agents with semi-supervision.

Factorial Agent Markov Model: Modeling Other Agents' Behavior in presence of Dynamic Latent Decision Factors

**Read paper on the following link:**

https://ifaamas.org/Proceedings/aamas2022/pdfs/p1648.pdf

**Abstract:**



REFORM: Reputation Based Fair and Temporal Reward Framework for Crowdsourcing


Online bipartite-matching platforms are ubiquitous and find applications in important areas such as crowdsourcing and ridesharing. In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching. The design of algorithms for such platforms has traditionally focused on the operator’s (expected) profit. Recent reports have shown that certain demographic groups may receive less favorable treatment under pure profit maximization. As a result, a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator’s profit. In this paper, we generalize the existing work to offer fair treatment guarantees to both sides of the market simultaneously, at a calculated worst case drop to operator profit. We consider group and individual Rawlsian fairness criteria. Moreover, our algorithms have theoretical guarantees and have adjustable parameters that can be tuned as desired to balance the trade-off between the utilities of the three sides. We also derive hardness results that give clear upper bounds over the performance of any algorithm.

Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual


Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted. Many conferences also design and run experiments on their paper review process, where some papers are assigned reviewers who provide reviews under an experimental condition. In this paper, we consider the question: how should reviewers be divided between phases or conditions in order to maximize total assignment similarity? We make several contributions towards answering this question. First, we prove that when the set of papers requiring additional review is unknown, a simplified variant of this problem is NP-hard. Second, we empirically show that across several datasets pertaining to real conference data, dividing reviewers between phases/conditions uniformly at random allows an assignment that is nearly as good as the oracle optimal assignment. This uniformly random choice is practical for both the two-phase and conference experiment design settings. Third, we provide explanations of this phenomenon by providing theoretical bounds on the suboptimality of this random strategy under certain natural conditions. From these easily-interpretable conditions, we provide actionable insights to conference program chairs about whether a random reviewer split is suitable for their conference.

Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design

**Read paper on the following link:**

https://ifaamas.org/Proceedings/aamas2022/pdfs/p1738.pdf

**Abstract:**



Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning

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