(No) more design patterns for multi-agent systems
Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE!'11, AOOPES'11, NEAT'11, & VMIL'11
Minimizing vehicular travel times using the multi-agent system beejama
PROFES'12 Proceedings of the 13th international conference on Product-Focused Software Process Improvement
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Simulation of coordinated anticipatory vehicle routing strategies on MATSim
PRIMA'11 Proceedings of the 14th international conference on Agent Based Simulation for a Sustainable Society and Multi-agent Smart Computing
A Preliminary Study on Anticipatory Stigmergy for Traffic Management
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
A generic material flow control model applied in two industrial sectors
Computers in Industry
Anticipatory routing of police helicopters
Expert Systems with Applications: An International Journal
Multi-agent Infrastructure Assisting Navigation for First Responders
Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science
Engineering Applications of Artificial Intelligence
An intelligent route management system for electric vehicle charging
Integrated Computer-Aided Engineering
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Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.