Large Pheromones: A Case Study with Multi-agent Physical A*

Felner, Ariel; Shoshani, Yaron; Wagner, Israel A.; Bruckstein, Alfred M.

doi:10.1007/978-3-540-28646-2_36

Search citation statements

Order By: Relevance

Paper Sections

Select...

Swarm Intelligence For Physical Environments -Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2005

2008

Publication Types

Select...

Book2

Article1

Other1

Relationship

Self Cite1

Independent3

Authors

Journals

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to this approach, the works of [44] and [45] present a mechanism which find the shortest path within a physical graph, while assuming several communication mechanism.…”

Section: Swarm Intelligence For Physical Environments -Related Workmentioning

confidence: 99%

Swarm Intelligent Systems

Nedjah¹,

Mourelle²

2006

Studies in Computational Intelligence

View full text Add to dashboard Cite

“…Similar to this approach, the works of [44] and [45] present a mechanism which find the shortest path within a physical graph, while assuming several communication mechanism.…”

Section: Swarm Intelligence For Physical Environments -Related Workmentioning

confidence: 99%

Swarm Intelligent Systems

Nedjah¹,

Mourelle²

2006

Studies in Computational Intelligence

View full text Add to dashboard Cite

Swarm Intelligence — Searchers, Cleaners and Hunters

Altshuler

Yanovsky

Wagner

et al.

Swarm Intelligent Systems

View full text Add to dashboard Cite

Multi-agent Physical A* with Large Pheromones

Felner

Shoshani

Wagner

et al. 2005

Auton Agent Multi-Agent Syst

Self Cite

View full text Add to dashboard Cite

Physical A* (PHA*) and its multi-agent version MAPHA* are algorithms that find the shortest path between two points in an unknown real physical environment with one or many mobile agents [A. Felner et al. Previous work assumed a complete sharing of knowledge between agents. Here we apply this algorithm to a more restricted model of communication which we call large pheromones, where agents communicate by writing and reading data at nodes of the graph that constitutes their environment. Previous works on pheromones usually assumed that only a limited amount of data can be written at each node. The large pheromones model assumes no limitation on the size of the pheromones and thus each agent can write its entire knowledge at a node. We show that with this model of communication the behavior of a multi-agent system is almost as good as with complete knowledge sharing. Under this model we also introduce a new type of agent, a communication agent, that is responsible for spreading the knowledge among other agents by moving around the graph and copying pheromones. Experimental results show that the contribution of communication agents is rather limited as data is already spread to other agents very well with large pheromones.

show abstract

Large Pheromones: A Case Study with Multi-agent Physical A*

Cited by 4 publications

References 8 publications

Swarm Intelligent Systems

Swarm Intelligent Systems

Swarm Intelligence — Searchers, Cleaners and Hunters

Multi-agent Physical A* with Large Pheromones

Contact Info

Product

Resources

About