Multi agent negotiation model for distributed task allocation

2010 ◽  
Author(s):  
He Luo ◽  
Xiao-jian Hu ◽  
Xiao-xuan Hu
Keyword(s):  
Task Allocation ◽  
Negotiation Model ◽  
Agent Negotiation ◽  
Multi Agent ◽  
Distributed Task Allocation

Multi-Agent Negotiation Paradigm for Agent Selection in B2C E-Commerce

2012 ◽  
pp. 164-186
Author(s):  
Bireshwar Dass Mazumdar ◽  
Swati Basak ◽  
Neelam Modanwal
Keyword(s):  
Cognitive Model ◽  
Multi Agent System ◽  
Agent System ◽  
Negotiation Model ◽  
Negotiation Strategies ◽  
Agent Negotiation ◽  
Customer Relation Management ◽  
Agent Selection ◽  
Multi Agent

Multi agent system (MAS) model has been extensively used in the different tasks of E-Commerce such as customer relation management (CRM), negotiation and brokering. The objective of this paper is to evaluate a seller agent’s various cognitive parameters like capability, trust, and desire. After selecting a best seller agent from ordering queue, it applies negotiation strategies to find the most profitable proposal for both buyer and seller. This mechanism belongs to a semi cooperative negotiation type, and selecting a seller and buyer agent pair using mental and cognitive parameters. This work provides a logical cognitive model, logical negotiation model between buyer agent and selected seller agent.

scholarly journals A Social Multi-Agent Cooperation System Based on Planning and Distributed Task Allocation

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 271
Author(s):  
Atef Gharbi
Keyword(s):  
Task Allocation ◽  
Autonomous Agents ◽  
Computational Time ◽  
Multi Agent System ◽  
Model Checker ◽  
Computation Tree ◽  
Message Exchange ◽  
Multi Agent ◽  
Distributed Task Allocation ◽  
Cooperation System

Planning and distributed task allocation are considered challenging problems. To address them, autonomous agents called planning agents situated in a multi-agent system should cooperate to achieve planning and complete distributed tasks. We propose a solution for distributed task allocation where agents dynamically allocate the tasks while they are building the plans. We model and verify some properties using computation tree logic (CTL) with the model checker its-ctl. Lastly, simulations are performed to verify the effectiveness of our proposed solution. The result proves that it is very efficient as it requires little message exchange and computational time. A benchmark production system is used as a running example to explain our contribution.

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