CROS: A Contingency Response multi-agent system for Oil Spills situations

Authors:
Aitor Mata;Juan M. Corchado;Dante I. Tapia
Affiliations:
Department of Computer Science, University of Salamanca, Plaza de la Merced s/n, Salamanca, Spain;Department of Computer Science, University of Salamanca, Plaza de la Merced s/n, Salamanca, Spain;Department of Computer Science, University of Salamanca, Plaza de la Merced s/n, Salamanca, Spain
Venue:
Applied Soft Computing
Year:
2011

Citing 23
Cited 0

Growing cell structures—a self-organizing network for unsupervised and supervised learning

Neural Networks
Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
Rational software agents: from theory to practice

Agent technology
Unraveling the Web Services Web: An Introduction to SOAP, WSDL, and UDDI

IEEE Internet Computing
FSfRT: Forecasting System for Red Tides

Applied Intelligence
Agent assistance for 3D world navigation

Lecture Notes in Computer Science
The Explanatory Power of Symbolic Similarity in Case-Based Reasoning

Artificial Intelligence Review
Agent-Based Distributed Resource Allocation in Technical Dynamic Systems

DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
Applying lazy learning algorithms to tackle concept drift in spam filtering

Expert Systems with Applications: An International Journal
Coalition formation mechanism in multi-agent systems based on genetic algorithms

Applied Soft Computing
MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students

Applied Soft Computing
Hybrid multi-agent architecture as a real-time problem-solving model

Expert Systems with Applications: An International Journal
Enabling run-time composition and support for heterogeneous pervasive multi-agent systems

Journal of Systems and Software
Fast Iterative Kernel Principal Component Analysis

The Journal of Machine Learning Research
Intelligent environment for monitoring Alzheimer patients, agent technology for health care

Decision Support Systems
Threshold accepting trained principal component neural network and feature subset selection: Application to bankruptcy prediction in banks

Applied Soft Computing
SHOMAS: Intelligent guidance and suggestions in shopping centres

Applied Soft Computing
PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis

Applied Soft Computing
Hyperspectral image analysis using genetic programming

Applied Soft Computing
A role-based mobile-agent approach to support e-democracy

Applied Soft Computing
Compressive-projection principal component analysis

IEEE Transactions on Image Processing
A Randomized Algorithm for Principal Component Analysis

SIAM Journal on Matrix Analysis and Applications
Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents CROS, a Contingency Response multi-agent system for Oil Spill situations. The system uses the Case-Based Reasoning methodology to generate predictions to determine the probability of finding oil slicks in certain areas of the ocean. CBR uses past information to generate new solutions to the current problem. The system employs a SOA-based multi-agent architecture so that the main components of the system can be remotely accessed. Therefore, all functionalities (applications and services) can communicate in a distributed way, even from mobile devices. The core of the system is a group of deliberative agents acting as controllers and administrators for all applications and services. CROS manages information such as sea salinity, sea temperature, wind speed, ocean currents and atmosphere pressure, obtained from several sources, including satellite images. The system has been trained using historical data obtained after the Prestige accident on the Galician west coast of Spain. Results have demonstrated that the system can accurately predict the presence of oil slicks in determined zones after an oil spill. The use of a distributed multi-agent architecture has been shown to enhance the overall performance of the system.