TY - GEN
T1 - Distributed navigation in an unknown physical environment
AU - Gilboa, Arnon
AU - Meisels, Amnon
AU - Felner, Ariel
PY - 2006
Y1 - 2006
N2 - We address the problem of navigating from an initial node to a goal node by a group of agents in an unknown physical environment. In such environments mobile agents must physically move around to discover the existence of nodes and edges. We assume that agents communicate by exchanging messages about their discoveries, their current locations and their intended plans. We also assume that an agent can only communicate with a predefined set of neighboring agents. A distributed algorithm, which is run independently by each agent, is presented. Given the current knowledge of the agent about the environment and the positions and intentions of other agents, the algorithm instructs the agent where to go next. An experimental evaluation of the algorithm is presented, with constrained and liberal neighborhood schemes. Results show that it is more beneficial to have a constrained neighborhood scheme because with this scheme the distributed intelligent behavior of agents generates a spread of knowledge throughout the environment more efficiently. Agents reach the goal node fast and the length of the path that they find is very close to that of the optimal path.
AB - We address the problem of navigating from an initial node to a goal node by a group of agents in an unknown physical environment. In such environments mobile agents must physically move around to discover the existence of nodes and edges. We assume that agents communicate by exchanging messages about their discoveries, their current locations and their intended plans. We also assume that an agent can only communicate with a predefined set of neighboring agents. A distributed algorithm, which is run independently by each agent, is presented. Given the current knowledge of the agent about the environment and the positions and intentions of other agents, the algorithm instructs the agent where to go next. An experimental evaluation of the algorithm is presented, with constrained and liberal neighborhood schemes. Results show that it is more beneficial to have a constrained neighborhood scheme because with this scheme the distributed intelligent behavior of agents generates a spread of knowledge throughout the environment more efficiently. Agents reach the goal node fast and the length of the path that they find is very close to that of the optimal path.
KW - Distributed navigation
KW - Mobile agents
UR - http://www.scopus.com/inward/record.url?scp=34247247239&partnerID=8YFLogxK
U2 - 10.1145/1160633.1160735
DO - 10.1145/1160633.1160735
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:34247247239
SN - 1595933034
SN - 9781595933034
T3 - Proceedings of the International Conference on Autonomous Agents
SP - 553
EP - 560
BT - Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
T2 - Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Y2 - 8 May 2006 through 12 May 2006
ER -