UNCERTAIN SPATIO-TEMPORAL REASONING FOR DISTRIBUTED TRANSPORTATION SCHEDULING PROBLEM

Abstract

Distributed artificial intelligence (DAI) is suitable for applications where there is no central control. One of these applications with which we are concerned is transportation scheduling. We noticed that all the approaches dedicated to this application use a weak representation of time and a simple reasoning. Furthermore, these approaches ignore the uncertain behavior of agents. What we propose is an approach based on fuzzy temporal characteristic functions (FTCF), which allows a powerful representation of agent company behaviors informing us at each time about the degree that the agent is available. Thanks to this representation, we develop a spatio-temporal reasoning allowing a cooperation inter-and intra-companies to allocate trucks and delegate orders. First, we use an offline scheduling algorithm that takes charge of new transportation tasks only when trucks are at a destination. To increase the performance of the system, we need to introduce the ability to take charge of the new transportation task immediately and to delegate it to a truck while this latter is moving toward a destination. For this purpose, the system needs to address two issues: (1) determining the current location of the truck and its proximity to the departure and arrival of the new transportation task and (2) respecting the temporal constraints. For this purpose, we use a spatio-temporal reasoning allowing us to deal with the first issue using a spatial reasoning and then addressing the second issue using a temporal reasoning. This approach is a first step towards a design of a temporally situated multi-agent system that allows us to take location and the time at which agents are at this location to determine the suitable actions.