A reactive agent-based problem-solving model: Application to localization and tracking

Authors:
Franck Gechter;Vincent Chevrier;François Charpillet
Affiliations:
UTBM-SeT Laboratory, Belfort Cedex;Loria-UHP Nancy 1, Vandoeuvre cedex;LORIA-INRIA Lorraine, Vandoeuvre cedex
Venue:
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Year:
2006

Citing 15
Cited 5

Flocks, herds and schools: A distributed behavioral model

SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
From Tom Thumb to the Dockers: some experiments with foraging robots

Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Monte Carlo localization: efficient position estimation for mobile robots

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Swarm intelligence: from natural to artificial systems

Swarm intelligence: from natural to artificial systems
Swarm intelligence

Swarm intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence

Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
How social spiders inspired an approach To region detection

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Heterogeneous Teams of Modular Robots for Mapping and Exploration

Autonomous Robots
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Bildbereichsbasierte Verfolgung von Straßenfahrzeugen durch adaptive Schätzung und Segmentierung von Optischen-Fluß-Feldern

Mustererkennung 1998, 20. DAGM-Symposium
Vision based localisation for a mobile robot

ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Engineering Self-Organising Systems

Engineering Self-Organising Systems
Programming stigmergic coordination with the TOTA middleware

Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Environments for multiagent systems state-of-the-art and research challenges

E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
An environment-based methodology to design reactive multi-agent systems for problem solving

E4MAS'05 Proceedings of the 2nd international conference on Environments for Multi-Agent Systems

Engineering of Software-Intensive Systems: State of the Art and Research Challenges

Software-Intensive Systems and New Computing Paradigms
Location problems optimization by a self-organizing multiagent approach

Multiagent and Grid Systems - Engineering Environments in Multiagent Systems
Evaluation of global system state thanks to local phenomenona

Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
An approach for the integration of swarm intelligence in MAS: An engineering perspective

Expert Systems with Applications: An International Journal
Generation of an adaptive simulation driven by product trajectories

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

For two decades, multi-agent systems have been an attractive approach for problem solving and have been applied to a wide range of applications. Despite the lack of generic methodology, the reactive approach is interesting considering the properties it provides. This article presents a problem-solving model based on a swarm approach where agents interact using physics-inspired mechanisms. The initial problem and its constraints are represented through agents' environment, the dynamics of which is part of the problem-solving process. This model is then applied to localization and target tracking. Experiments assess our approach and compare it to widely-used classical algorithms.