Coordination ontology for multi agent based distributed decision making

2012 ◽  
Author(s):  
Sudipta Acharya ◽  
Animesh Dutta
Keyword(s):  
Decision Making ◽  
Agent Based ◽  
Distributed Decision Making ◽  
Multi Agent ◽  
Distributed Decision

Multiagent Based Dynamic Resource Control for Decentralized Multi-Project Using Combinatorial Exchange

2012 ◽  
Vol 532-533 ◽  
pp. 566-570
Author(s):  
Lei Wang ◽  
De Chen Zhan ◽  
Lan Shun Nie ◽  
Dian Hui Chu ◽  
Xiao Fei Xu
Keyword(s):  
Decision Making ◽  
Optimization Method ◽  
Resource Control ◽  
Modern Times ◽  
Distributed Decision Making ◽  
Project Environment ◽  
Multi Agent ◽  
Dynamic Resource ◽  
Negotiation Mechanism ◽  
Distributed Decision

Decentralized multi-project environment is very common in modern times, and the dynamic resource control problem for this project environment has attracted more attention. Traditional optimization method for multi-project based on the centralization in decision making does not suit for solving this problem any more. In this paper, we analyze the distributed decision making process for the dynamic resource control in the decentralized multi-project environment, and present a multi-agent system model for this problem. Using combinatorial exchange based on market, we design a negotiation mechanism to cope with the time disruptions in the stage of project execution. Computational results show that the combinatorial exchange mechanism could solve the problem effectively and has a powerful controllability for the different weights of the multiple projects.

scholarly journals Synchronization of Local Plans for Cooperative Distributed Decision-Making

2021 ◽  
Author(s):  
Maximilian Kloock ◽  
Bassam Alrifaee
Keyword(s):  
Decision Making ◽  
Predictive Control ◽  
Local System ◽  
Multi Agent Systems ◽  
Local Planning ◽  
Distributed Decision Making ◽  
Planning Step ◽  
Multi Agent ◽  
Cooperative Decision Making ◽  
Distributed Decision

In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.

scholarly journals Synchronization of Local Plans for Cooperative Distributed Decision-Making

2021 ◽  
Author(s):  
Maximilian Kloock ◽  
Bassam Alrifaee
Keyword(s):  
Decision Making ◽  
Predictive Control ◽  
Local System ◽  
Multi Agent Systems ◽  
Local Planning ◽  
Distributed Decision Making ◽  
Planning Step ◽  
Multi Agent ◽  
Cooperative Decision Making ◽  
Distributed Decision

In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.

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