An intelligent spatial land use planning support system using socially rational agents. Issue 5 (4th May 2017)

• Record Type: Journal Article
• Authors: Ghavami, Seyed Morsal; Taleai, Mohammad; Arentze, Theo
• Is Part Of: International journal of geographical information science. Volume 31:Issue 5(2017)
• Page Start: 1022
• Page End: 1041
• Publication Date: 2017-05-04
• Subjects: Intelligent planning support system -- Bayesian learning -- social value orientation -- land use planning -- multi-agent system; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2016.1263306
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors' social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors' social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained. This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents.