Mykhaylo Petryk, Mykola Ivanchov, Sebastian Leclerc, Daniel Canet and Jacques Fraissard (December 12th 2019). Competitive Adsorption and Diffusion of Gases in a Microporous Solid [Online First], IntechOpen, DOI: 10.5772/intechopen.88138.

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). 

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation. IJCCI is composed of three co-located conferences, each specialized in at least one of the aforementioned main knowledge areas.

IJCCI 2019

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation.

We define noisy combinatorial markets, a specific class of combinatorial markets and introduce a model to study them. We present a learning methodology we developed to learn competitive equilibria of noisy combinatorial markets. We summarize preliminary experimental results to evaluate the performance of our learning algorithms.


Learning Competitive Equilibria  in Noisy Combinatorial Markets

technical paper

Learning Competitive Equilibria  in Noisy Combinatorial Markets

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Optimising a Real-Time Scheduler for Railway Lines using Policy Search

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Learning Competitive Equilibria in Noisy Combinatorial Markets

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Next from AAMAS 2020

Optimising a Real-Time Scheduler for Railway Lines using Policy Search

Similar lecture

From Machine Learning to Explainable AI and Beyond

Learning Competitive Equilibria  in Noisy Combinatorial Markets