0% Complete
Home
/
11th International Conference on Computer and Knowledge Engineering
A Formalism for Specifying Capability-based Task Allocation in MAS
Authors :
Samaneh HoseinDoost
1
Bahman Zamani
2
Afsaneh Fatemi
3
1- MDSE Research Group, Faculty of Computer Engineering, University of Isfahan
2- MDSE Research Group, Faculty of Computer Engineering University of Isfahan
3- MDSE Research Group, Faculty of Computer Engineering University of Isfahan
Keywords :
Task Allocation, Capability-Based Task Allocation, Multi-Agent System (MAS), Formalism, Mathematical Modeling
Abstract :
Task allocation, as an important issue in multi-agent systems (MAS), is defined as allocating the tasks to the agents such that maximum tasks are performed in minimum time. The vast range of application domains, such as scheduling, cooperation in crisis management, and project management, deal with the task allocation problem. Despite the plethora of algorithms that are proposed to solve this problem in different application domains, research on proposing a formalism for this problem is scarce. Such a formalism can be used as a way for better understanding and analyzing the behavior of real-world systems. In this paper, we propose a new formalism for specifying capability-based task allocation in MAS. The formalism can be used in different application domains to help domain experts better analyze and test their algorithms with more precision. To show the applicability of the formalism, we consider two algorithms as the case studies and formalize the inputs and outputs of these algorithms using the proposed formalism. The results indicate that our formalism is promising for specifying the capability-based task allocation in MAS at a proper level of abstraction.
Papers List
List of archived papers
UAV-based Firefighting by Multi-agent Reinforcement Learning
Reza Shami Tanha - Mohsen Hooshmand - Mohsen Afsharchi
Cardiology Disease Diagnosis by Analyzing Histological Microscopic Images Using Deep Learning
Maria Salehpanah - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Sajad Rezaei
Predicting cascading failure with machine learning methods in the interdependent networks
Mohamad Hossein Maghsoodi - Mohamad Khansari
Segmentation of Hard Exudates in Retinal Fundus Images Using BCDU-Net
Nafise Ameri - Nasser Shoeibi - Mojtaba Abrishami
An Ensemble CNN for Brain Age Estimation based on Hippocampal Region Applicable to Alzheimer's Diagnosis
Zahra Qodrati - Seyedeh Masoumeh Taji - Habibollah Danyali - Kamran Kazemi
Lightweight Local Transformer for COVID-19 Detection Using Chest CT Scans
Hojat Asgarian Dehkordi - Hossein Kashiani - Amir Abbas Hamidi Imani - Shahriar Baradaran Shokouhi
Sports News Summarization Using Ensebmle Learning
Moein Sartakhti.salimi@gmail.com - Mohammad Javad Maleki Kahaki - Ahmad Yoosofan - Seyyed Vahid Moravvej
Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network
Amirhossein Ghaemi - Seyyed Amir Mousavi mobarakeh - Habibollah Danyali - Kamran Kazemi
Multi-source Ensemble Model for Scene Recognition
Amir Hossein Saleknia - Ahmad Ayatollahi
Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks
Mehrdad Mohammadian - Neda Maleki - Tobias Olsson - Fredrik Ahlgren
more
Samin Hamayesh - Version 42.4.1