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A multi-agent approach for planning activities in decentralized electricity markets

Publications associées (31)

Impact of Redundancy on Resilience in Distributed Optimization and Learning

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

Assoc Computing Machinery2023

Discovering Influencers in Opinion Formation Over Social Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia, Mert Cemri

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Nirupam Gupta

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

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Byzantine-faulty agents, which may not follow a prescr ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, James Gonzalo King, Rajnish Ranjan, Armando Romani, Mustafa Anil Tuncel, Aleksandra Zuzanna Teska, Polina Litvak

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

2024

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Ali H. Sayed, Virginia Bordignon, Stefan Vlaski

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

François Maréchal, Yuxuan Li

The load tracking performance of combined cooling, heating, and power multi-energy system (CCHP-MES) greatly depends on the equipment capacity configuration. And the frequent fluctuations in the source-load uncertainty puts higher demands on the load track ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Nirupam Gupta, Shuo Liu

Assoc Computing Machinery2023

Discovering Influencers in Opinion Formation Over Social Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia, Mert Cemri

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Partial Information Sharing Over Social Learning Networks

Ali H. Sayed, Virginia Bordignon

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

The neuromechanics of animal locomotion: From biology to robotics and back

Auke Ijspeert, Pavan P Ramdya

Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating anima ...

AMER ASSOC ADVANCEMENT SCIENCE2023

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Olga Fink, Mina Montazeri

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

PUBLIC LIBRARY SCIENCE2023

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Nirupam Gupta

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Henry Markram, Eilif Benjamin Muller, Srikanth Ramaswamy, Michael Reimann, James Gonzalo King, Rajnish Ranjan, Armando Romani, Mustafa Anil Tuncel, Aleksandra Zuzanna Teska, Polina Litvak

2024

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Ali H. Sayed, Virginia Bordignon, Stefan Vlaski

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

François Maréchal, Yuxuan Li

PERGAMON-ELSEVIER SCIENCE LTD2023