Finding Geospatial Resources Using Uncertain Data

摘要

In this paper, we address the problem of finding a resource in a probabilistic setting. In this problem, there are spatially located static resources and a mobile agent. The agent looks to obtain one of the resources while minimizing the cost. This cost may consist of several sub-costs the agent has to pay, from travel time to monetary cost of using a resource. We assume that the agent has no knowledge of exact availability of the resources in real-time, but some prior or partial data gives estimations of this information. This model applies to many situations that arise in urban transportation systems, such as drivers looking for street parking, or taxis looking for new customers. Our approach to the resource search problem only employs uncertain information about resource availability, minimizes the expected cost, and utilizes concepts from decision theory.