Machine Learning based Multi-Agent System for Detecting and Neutralizing Unseen Cyber-Attacks in AGC and HVDC Systems

2022 ◽  
pp. 1-1
Author(s):  
Siddhartha Deb Roy ◽  
Sanjoy Debbarma ◽  
Josep M. Guerrero
Keyword(s):  
Machine Learning ◽  
Cyber Attacks ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations

2019 ◽  
Author(s):  
Tiago Pinto ◽  
Zita Vale
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Electricity Markets ◽  
Evolutionary Computing ◽  
Multi Agent System ◽  
Agent System ◽  
Agent Based ◽  
Multi Agent ◽  
Competitive Electricity Markets

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

scholarly journals Machine Learning in a Multi-Agent System for Distributed Computing Management

2018 ◽  
Author(s):  
I V Bychkov ◽  
◽  
A G Feoktistov ◽  
I A Sidorov ◽  
A V Edelev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals A Negotiation Optimization Strategy of Collaborative Procurement with Supply Chain Based on Multi-Agent System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Chouyong Chen ◽  
Chao Xu
Keyword(s):  
Machine Learning ◽  
Adaptive Learning ◽  
Learning Effect ◽  
Multi Agent System ◽  
Optimization Strategy ◽  
Agent System ◽  
Negotiation Model ◽  
Multi Agent ◽  
Learning Machine ◽  
Self Adaptive

In the process of collaborative procurement, buyers and suppliers are prone to conflict in cooperation due to differences in needs and preferences. Negotiation is a crucial way to resolve the conflict. Aimed at ameliorating the situations of underdeveloped self-adaptive learning effect of current collaborative procurement negotiation, this paper constructs a negotiation model based on multi-agent system and proposes a negotiation optimization strategy combined with machine learning. It provides a novel perspective for the analysis of intelligent SCM. The experimental results suggest that the proposed strategy improves the success rate of self-adaptive learning and joint utility of agents compared with the strategy of single learning machine, and it achieves win-win cooperation between purchasing enterprise and supplier.

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