Multiagent Approach for the Representation of Information in a Decision Support System

TL;DR

This paper presents a multiagent decision support system designed to efficiently represent crisis situation information, aiding actors in rapid and effective emergency response through semantic feature interactions.

Contribution

It introduces a multiagent architecture with factual agents that represent partial situation data using semantic features and ontologies, enhancing crisis information management.

Findings

Agents develop through interactions based on semantic proximity.

The system effectively models complex crisis situations.

Semantic features enable precise situation representation.

Abstract

In an emergency situation, the actors need an assistance allowing them to react swiftly and efficiently. In this prospect, we present in this paper a decision support system that aims to prepare actors in a crisis situation thanks to a decision-making support. The global architecture of this system is presented in the first part. Then we focus on a part of this system which is designed to represent the information of the current situation. This part is composed of a multiagent system that is made of factual agents. Each agent carries a semantic feature and aims to represent a partial part of a situation. The agents develop thanks to their interactions by comparing their semantic features using proximity measures and according to specific ontologies.