Applying Machine Learning in Designing Distributed Auction for Multi-agent Task Allocation with Budget Constraints

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

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Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Energies ◽

10.3390/en14123654 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3654

Author(s):

Nastaran Gholizadeh ◽

Petr Musilek

Keyword(s):

Machine Learning ◽

Power Systems ◽

Learning Algorithm ◽

Single Point ◽

Distributed Learning ◽

Large Data ◽

Multi Agent Systems ◽

Power Quality Monitoring ◽

Multi Agent ◽

Learning Frameworks

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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Market Clearing–based Dynamic Multi-agent Task Allocation

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3356467 ◽

2020 ◽

Vol 11 (1) ◽

pp. 1-25

Author(s):

Sofia Amador Nelke ◽

Steven Okamoto ◽

Roie Zivan

Keyword(s):

Task Allocation ◽

Market Clearing ◽

Multi Agent

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Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

Applied Sciences ◽

10.3390/app9102117 ◽

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.

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

IEEE Journal on Emerging and Selected Topics in Circuits and Systems ◽

10.1109/jetcas.2022.3142055 ◽

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

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A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418813037 ◽

2018 ◽

Vol 15 (6) ◽

pp. 172988141881303 ◽

Cited By ~ 1

Author(s):

Bing Xie ◽

Xueqiang Gu ◽

Jing Chen ◽

LinCheng Shen

Keyword(s):

Coalition Formation ◽

Task Allocation ◽

Game Model ◽

Multi Agent Systems ◽

Coalition Game ◽

Task Execution ◽

Agent Systems ◽

Dynamic Task ◽

Dynamic Task Allocation ◽

Multi Agent

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

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