Paper Info
Title
Multi-agent Simulation Design Driven by Real Observations and Clustering Techniques
Abstract
The multi-agent simulation consists in using a set of interacting agents to reproduce the dynamics and the evolution of the phenomena that we seek to simulate. It is considered now as an alternative to classical simulations based on analytical models. But, its implementation remains difficult, particularly in terms of behaviors extraction and agents modelling. This task is usually performed by the designer who has some expertise and available observation data on the process. In this paper, we propose a novel way to make use of the observations of real world agents to model simulated agents. The modelling is based on clustering techniques. Our approach is illustrated through an example in which the behaviors of agents are extracted as trajectories and destinations from video sequences analysis. This methodology is investigated with the aim to apply it, in particular, in a retail space simulation for the evaluation of marketing strategies. This paper presents experiments of our methodology in the context of a public area modelling.
Year
DOI
Venue
2011
10.1109/ICTAI.2011.89
Tools with Artificial Intelligence
Keywords
Field
DocType
image sequences,interactive systems,marketing,multi-agent systems,pattern clustering,simulation,clustering techniques,interacting agents,marketing strategies,multi-agent simulation design,real observations,video sequences analysis,Automatic Agent Design,Behavior modelling,Clustering,Data Mining,Multi-Agent Simulation
Data modeling,Data mining,Simulation design,Computer science,Pattern clustering,Space simulator,Multi-agent system,Artificial intelligence,Cluster analysis,Machine learning,Trajectory
Conference
ISSN
ISBN
Citations 
1082-3409 E-ISBN : 978-0-7695-4596-7
978-0-7695-4596-7
2
PageRank 
References 
Authors
0.39
5
4
Name
Order
Citations
PageRank
Imen Saffar120.39
Arnaud Doniec214415.23
Jacques Boonaert3336.02
Stéphane Lecoeuche461.46