Spain

ECAI 2020

Monte Carlo Information-Oriented Planning

planning under uncertainty

active sensing

partial observability

mcts

pomdp

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 2022

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).

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Enhanced foraging in robot swarms using collective Lévy walks

General Supervision via Probabilistic Transformations

Generating Ensembles of Multi-Label Classifiers Using Cooperative Coevolutionary Algorithms

Heuristics for Link Prediction in Multiplex Networks

SSHFD: Single Shot Human Fall Detection with Occluded Joints Resilience

Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

EPNE: Evolutionary Pattern Preserving Network Embedding

Lifting Majority to Unanimity in Opinion Diffusion

Dynamic thresholding for learning sparse neural networks

A Region Selection Model to Identify Unknown Unknowns in Image Datasets

https://d3smihljt9218e.cloudfront.net/transcript/999/subtitle/2507d6e3048fd4af11e857cf1c9d96d4.vtt

English (automatic)

Monte Carlo Information-Oriented Planning

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Enhanced foraging in robot swarms using collective Lévy walks

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Monte Carlo Information-Oriented Planning

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Next from ECAI 2020

Enhanced foraging in robot swarms using collective Lévy walks

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BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs

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