New Zealand

Ümit Ulusoy, Mehmet Serdar Güzel and Erkan Bostanci (April 22nd 2020). A Q-Learning-Based Approach for Simple and Multi-Agent Systems, Multi Agent Systems - Strategies and Applications, Ricardo López - Ruiz, IntechOpen, DOI: 10.5772/intechopen.88484.

Dennis Barrios-Aranibar and Luiz M. G. Gon&#231;alves (January 1st 2009). Influence Value Q-Learning: A Reinforcement Learning Algorithm for Multi Agent Systems, Theory and Novel Applications of Machine Learning, Meng Joo Er and Yi Zhou, IntechOpen, DOI: 10.5772/6675. 

Xiaonan Dong, Paolo Stegagno, Chengzhi Yuan and Wei Zeng (April 22nd 2020). Cooperative Adaptive Learning Control for a Group of Nonholonomic UGVs by Output Feedback, Multi Agent Systems - Strategies and Applications, Ricardo López - Ruiz, IntechOpen, DOI: 10.5772/intechopen.87038.

AAMAS 2020

Option-Critic in Cooperative Multi-agent Systems

cooperative multi-agent systems

temporal abstraction

common information approach

hierarchical reinforcement learning

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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AAMAS 2021

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society. This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events extracted from news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.

Mining International Political Norms from the GDELT Database

Designing Truthful Contexual Multi-Armed Bandits based Sponsored Search Auctions

Reinforcement learning algorithms have been shown to converge to the classic replicator dynamics of evolutionary game theory, which describe the evolutionary process in the limit of an infinite population. However, it is not clear how to interpret these dynamics from the perspective of a learning agent. In this paper we propose a data-inefficient batch-learning algorithm for temporal difference Q learning and show that it converges to a recently proposed deterministic limit of temporal difference reinforcement learning. In a second step, we state a data-efficient learning algorithm, that uses a form of experience replay, and show that it retains core features of the batch learning algorithm. Thus, we propose an agent-interpretation for the learning dynamics: What is the infinite population limit of evolutionary dynamics is the infinite memory limit of learning dynamics.

Reinforcement Learning Dynamics in the Infinite Memory Limit

Agent-Based Explanations in AI: Towards an Abstract Framework

On-line Estimators for Ad-hoc Task Allocation

"Cooperative cargo transport (e.g., two agents moving a table) is trivial for humans, but poses exceptional challenges to robots. One challenge is learning the dynamics properties of unknown cargo, which is critical for safe operations. We present an algorithm to estimate the inertial parameters of an object grasped by one or more robots in real-time. 
We model each robot's N sub-body system--- considering external and joint actuation--- using the Recursive Newton-Euler equations. A constrained Unscented Kalman Filter estimates the grasped object's mass, center of mass and moments of inertia. Our approach is validated through simulation using Astrobee, a free-flying robot."

Cooperative Real-Time Inertial Parameter Estimation

The problem of disinformation in online social networks has recently received a considerable amount of attention from the research community. It has been shown that online social networks are extensively getting exploited to alter public opinion and individuals’ stance on a wide-range of topics. This study proposes an agent-based model that simulates a disinformation campaign by a group of organized users called conspirators, targeting a susceptible population, which are then opposed by a parallel organized group of users referred to as inouclators that try to act as a barrier to the spread of disinformation. The results of this study indicate that the process of inoculating a susceptible population against disinformation is mostly at the price of further polarizing the population.

Modeling Disinformation and the Effort to Counter It: A Cautionary Tale of When the Treatment Can Be Worse Than the Disease

target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in the DeepMind Control Suite benchmark. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.

Deep Residual Reinforcement Learning

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**Abstract:**

Crowdsourcing harnesses human effort to solve computer-hard problems. Such tasks often have different levels of difficulty and workers have varying levels of skill at completing them. With a limited budget, it is important to wisely allocate the spend among the tasks and workers such that the overall outcome is as good as possible. Most existing work addresses this budget allocation problem by assuming that workers have a single level of ability for all tasks. However, this neglects the fact that tasks can belong to a variety of diverse categories and workers may have varying abilities across them. To incorporating such category-awareness, we model the interaction between the crowdsource campaign initiator and the workers as a procurement auction and propose a computationally efficient mechanism, INCARE, to achieve high-quality outcomes given a limited budget. In this presentation, we will present INCARE in detail and show the performance of INCARE on a standard real-world data set.

A Budget-Limited Mechanism for Category-Aware Crowdsourcing Systems

Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compositionality. We note that most of those works have focused on communicative bandwidth as being of primary importance. While important, it is not the only contributing factor. In this paper, we investigate the learning biases that affect the efficacy and compositionality of emergent languages. Our foremost contribution is to explore how capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community.

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