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

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.


AAMAS 2020

Learning Competitive Equilibria  in Noisy Combinatorial Markets

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). 

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 MAIN CONFERENCE

technical paper

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

Fragility and Robustness in Multiagent Systems

Interval Q-Learning: Balancing Deep and Wide Exploration

Deep reinforcement learning for large-scale epidemic control

Reward Redistribution Mechanism in Multi-Agent Reinforcement Learning

Option Encoder : A framework for Discovering a Policy Basis in Reinforcement Learning

Design and Evaluations of Multi-Agent Simulation Model for Electric Power Sharing among Households

Reinforcement Learning for Train Movement Planning at Railway Stations

Proportionality in Approval-Based Elections With a Variable Number of Winners

Preserving Condorcet Winners under Strategic Manipulation

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

Downloads

Next from AAMAS 2020

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

Similar lecture

GuCNet: A Guided Clustering-based Network for Improved Classification

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EVENTS

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Learning Competitive Equilibria  in Noisy Combinatorial Markets