Agent technologies for post-disaster urban planning

Abstract

Urban planning is a complex decision making process which must compensate for the various interests of multiple stakeholders with respect to physical, social, and economic constraints. Despite growing interest in using A.I. in urban design and planning this community remains a field dominated by human experts. Recent catastrophic disasters such as hurricane Katrina, however, have underscored the need for increased automation and more efficient urban design processes. One particularly urgent decision making in postdisaster urban planning is that of finding good locations for temporary housing. As an illustrative example of the potential of agent technologies in post-disaster planning contexts we propose an agent-based decision support system that can identify good candidate locations for a specific purpose. We showcase an application of our decision support system in pre-disaster mode that identifies a set of ideal locations for potential revitalization. We then discuss how this system can be extended to solve a problem of finding good locations for temporary housing in post-disaster mode. Our preliminary experimental results show promising potential of using agent technologies towards solving real life problems in the urban planning domain.