scholarly journals Multi-Agent Negotiation Strategies for Task Allocation Process in E-Commerce System

2016 ◽  
Vol V5 (04) ◽  
Author(s):  
Ndayizeye Prudence ◽  
W. Jeberson ◽  
Bd Mazumdar ◽  
Keyword(s):  
Task Allocation ◽  
Negotiation Strategies ◽  
Agent Negotiation ◽  
Allocation Process ◽  
Multi Agent

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.

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

2011 ◽  
Vol 3 (2) ◽  
pp. 33-52 ◽  
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.

Ant Colony Optimization for task allocation in Multi-Agent Systems

2013 ◽  
Vol 10 (3) ◽  
pp. 125-132 ◽  
Author(s):  
Lu Wang ◽  
Zhiliang Wang ◽  
Siquan Hu ◽  
Lei Liu
Keyword(s):  
Ant Colony Optimization ◽  
Task Allocation ◽  
Ant Colony ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

scholarly journals The best task allocation process is to decide on one’s own: effects of the allocation agent in human–robot interaction on perceived work characteristics and satisfaction

2020 ◽  
Author(s):  
Alina Tausch ◽  
Annette Kluge
Keyword(s):  
Task Allocation ◽  
New Technologies ◽  
Human Robot Interaction ◽  
Work Characteristics ◽  
Allocation Decision ◽  
Robot Interaction ◽  
Work Systems ◽  
Organizational Unit ◽  
Allocation Process ◽  
Task Identity

AbstractNew technologies are ever evolving and have the power to change human work for the better or the worse depending on the implementation. For human–robot interaction (HRI), it is decisive how humans and robots will share tasks and who will be in charge for decisions on task allocation. The aim of this online experiment was to examine the influence of different decision agents on the perception of a task allocation process in HRI. We assume that inclusion of the worker in the allocation will create more perceived work resources and will lead to more satisfaction with the allocation and the work results than a decision made by another agent. To test these hypotheses, we used a fictional production scenario where tasks were allocated to the participant and a robot. The allocation decision was either made by the robot, by an organizational unit, or by the participants themselves. We then looked for differences between those conditions. Our sample consisted of 151 people. In multiple ANOVAs, we could show that satisfaction with the allocation process, the solution, and with the result of the work process was higher in the condition where participants themselves were given agency in the allocation process compared to the other two. Those participants also experienced more task identity and autonomy. This has implications for the design of allocation processes: The inclusion of workers in task allocation can play a crucial role in leveraging the acceptance of HRI and in designing humane work systems in Industry 4.0.

Market Clearing–based Dynamic Multi-agent Task Allocation

2020 ◽  
Vol 11 (1) ◽  
pp. 1-25
Author(s):  
Sofia Amador Nelke ◽  
Steven Okamoto ◽  
Roie Zivan
Keyword(s):  
Task Allocation ◽  
Market Clearing ◽  
Multi Agent

scholarly journals Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2117
Author(s):  
Ming Chong Lim ◽  
Han-Lim Choi
Keyword(s):  
Task Allocation ◽  
Random Sampling ◽  
Scheduling Algorithm ◽  
Stochastic Algorithm ◽  
Speed Of Convergence ◽  
Temporal Dependence ◽  
Real World Applications ◽  
Coupled Constraints ◽  
Multi Agent ◽  
Feasible Solutions

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.

scholarly journals A Faithful Mechanism for Incremental Multi-Agent Agreement Problems with Self-Interested and Privacy-Preserving Agents

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Farzaneh Farhadi ◽  
Nicholas R. Jennings
Keyword(s):  
Information Leakage ◽  
Optimal Strategies ◽  
Privacy Preserving ◽  
Limited Attention ◽  
Limited Information ◽  
Multi Agent Systems ◽  
Negotiation Strategies ◽  
Multi Agent ◽  
Theoretical And Numerical Analysis ◽  
Negotiation Mechanism

AbstractDistributed multi-agent agreement problems (MAPs) are central to many multi-agent systems. However, to date, the issues associated with encounters between self-interested and privacy-preserving agents have received limited attention. Given this, we develop the first distributed negotiation mechanism that enables self-interested agents to reach a socially desirable agreement with limited information leakage. The agents’ optimal negotiation strategies in this mechanism are investigated. Specifically, we propose a reinforcement learning-based approach to train agents to learn their optimal strategies in the proposed mechanism. Also, a heuristic algorithm is designed to find close-to-optimal negotiation strategies with reduced computational costs. We demonstrate the effectiveness and strength of our proposed mechanism through both game theoretical and numerical analysis. We prove theoretically that the proposed mechanism is budget balanced and motivates the agents to participate and follow the rules faithfully. The experimental results confirm that the proposed mechanism significantly outperforms the current state of the art, by increasing the social-welfare and decreasing the privacy leakage.

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