Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Ant-inspired navigation in unknown environments
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
HAS-SOP: Hybrid Ant System for the Sequential Ordering Problem
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Digital pheromone mechanisms for coordination of unmanned vehicles
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
A Pheromone-Based Utility Model for Collaborative Foraging
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Towards a paradigm change in computer science and software engineering: a synthesis
The Knowledge Engineering Review
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Performance of pheromone model for predicting traffic congestion
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Formal Model for Situated Multi-Agent Systems
Fundamenta Informaticae - Multiagent Systems (FAMAS'03)
Real-time agent characterization and prediction
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
Dynamic Pricing Algorithms for Task Allocation in Multi-agent Swarms
Massively Multi-Agent Technology
Theoretical Study of Ant-based Algorithms for Multi-Agent Patrolling
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Hybrid multi-agent systems: integrating swarming and BDI agents
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Concurrent modeling of alternative worlds with polyagents
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
Extending time management support for multi-agent systems
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
Digital pheromones for coordination of unmanned vehicles
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
Information Sciences: an International Journal
Pheromone model: application to traffic congestion prediction
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
Evolutionary optimization of pheromone-based stigmergic communication
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
A Formal Model for Situated Multi-Agent Systems
Fundamenta Informaticae - Multiagent Systems (FAMAS'03)
Task partitioning via ant colony optimization for distributed assembly
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
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Agents guided by synthetic pheromones can imitate the behavior of insects in tasks such as path planning. These systems are well suited to problems such as path planning for unmanned robotic vehicles. We have developed a model for controlling robotic vehicles in combat missions using synthetic pheromones. In the course of our experimentation, we have identified the need for proper tuning of the algorithms to get the desired behavior. We briefly describe the synthetic pheromone mechanisms for dynamically finding targets and planning safe paths. Genetic algorithms for automatically tuning the behavior of the pheromone equations are described.