Multi-objective control for multi-agent systems using Lyapunov-like barrier functions

2013 ◽  
Author(s):  
Dimitra Panagou ◽  
Dusan M. Stipanovic ◽  
Petros G. Voulgaris
Keyword(s):  
Multi Agent Systems ◽  
Barrier Functions ◽  
Agent Systems ◽  
Multi Objective ◽  
Objective Control ◽  
Multi Agent

Approach for Negotiation Problems in Multi-Agent Systems for DC Micro Grid Using Multi-Objective Evolutionary Algorithms

2015 ◽  
Vol 713-715 ◽  
pp. 2106-2109
Author(s):  
Mauricio Mauledoux ◽  
Edilberto Mejía-Ruda ◽  
Oscar I. Caldas
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimal ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Objective ◽  
Multi Agent ◽  
Micro Grid ◽  
Optimal Space ◽  
Multi Agents

The work is devoted to solve allocation task problem in multi agents systems using multi-objective genetic algorithms and comparing the technique with methods used in game theories. The paper shows the main advantages of genetic algorithms and the way to apply a parallel approach dividing the population in sub-populations saving time in the search and expanding the coverage of the solution in the Pareto optimal space.

scholarly journals A utility-based analysis of equilibria in multi-objective normal-form games

2020 ◽  
Vol 35 ◽  
Author(s):  
Roxana Rădulescu ◽  
Patrick Mannion ◽  
Yijie Zhang ◽  
Diederik M. Roijers ◽  
Ann Nowé
Keyword(s):  
Normal Form ◽  
Optimization Criterion ◽  
Expected Returns ◽  
Objective Functions ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Normal Form Games ◽  
Multi Objective ◽  
Trade Offs ◽  
Multi Agent

Abstract In multi-objective multi-agent systems (MOMASs), agents explicitly consider the possible trade-offs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analyzed on the basis of the utility that these compromises have for the users of a system, where an agent’s utility function maps their payoff vectors to scalar utility values. This utility-based approach naturally leads to two different optimization criteria for agents in a MOMAS: expected scalarized returns (ESRs) and scalarized expected returns (SERs). In this article, we explore the differences between these two criteria using the framework of multi-objective normal-form games (MONFGs). We demonstrate that the choice of optimization criterion (ESR or SER) can radically alter the set of equilibria in a MONFG when nonlinear utility functions are used.

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