Multi-agent physical A * with large pheromones

Ariel Felner, Yaron Shoshani, Yaniv Altshuler, Alfred M. Bruckstein

Research output: Contribution to journalReview articlepeer-review

14 Scopus citations

Abstract

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. Journal of Artificial Intelligence Research, 21:631-679, 2004; A. Felner et al. Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy, 2002:240-247]. 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.

Original languageEnglish
Pages (from-to)3-34
Number of pages32
JournalAutonomous Agents and Multi-Agent Systems
Volume12
Issue number1
DOIs
StatePublished - Jan 2006
Externally publishedYes

Bibliographical note

Funding Information:
This research was partly supported by Israeli Ministry of Science Infrastructure grant No. 3-942. We would like to thank the anonymous referees for their enlighting comments and suggestions which greatly contributed to the quality of this paper.

Keywords

  • A *
  • Mobile agents
  • Multi-agent communication models
  • Pheromones
  • Shortest path

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