Bo Fan and Jiexin Pu (January 30th 2011). Multi-Robot Information Fusion and Coordination Based on Agent, Multi-Robot Systems, Trends and Development, Toshiyuki Yasuda, IntechOpen, DOI: 10.5772/13029.

Antonio J. R. Neves, Jose Luis Azevedo, Bernardo Cunha, Nuno Lau, Joao Silva, Frederico Santos, Gustavo Corrente, Daniel A. Martins, Nuno Figueiredo, Artur Pereira, Luis Almeida, Luis Seabra Lopes, Armando J. Pinho, Joao Rodrigues and Paulo Pedreiras (January 1st 2010). CAMBADA Soccer Team: from Robot Architecture to Multiagent Coordination, Robot Soccer, Vladan Papi&#263;, IntechOpen, DOI: 10.5772/7353.

Akinwale Akinwunmi, Emmanuel Olajubu and Ganiyu Aderounmu (February 7th 2019). Multi-Agent System Approach for Trustworthy Cloud Service Discovery, Multi-Agent Systems - Control Spectrum, Vladimir Shikhin, IntechOpen, DOI: 10.5772/intechopen.84291. 

New Zealand

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.

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AAMAS 2020

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). 

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents from their start locations to their goal locations without collisions. We study the lifelong variant of MAPF where agents are constantly engaged with new goal locations, such as in warehouses. We propose a new framework for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of agents only within a finite time horizon and ignores collisions beyond it. Our framework is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We evaluate our framework with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents, significantly outperforming existing methods.

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

technical paper

Matching Affinity Clustering: Improved Hierarchical Clustering at Scale with Guarantees

Option-Critic in Cooperative Multi-agent Systems

Mining International Political Norms from the GDELT Database

Designing Truthful Contexual Multi-Armed Bandits based Sponsored Search Auctions

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 Real-Time Inertial Parameter Estimation

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

Deep Residual Reinforcement Learning

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

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