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. 相似文献
Modern hyperspectral imaging and non-imaging spectroradiometer has the capability to acquire high-resolution spectral reflectance data required for surface materials identification and mapping. Spectral similarity metrics, due to their mathematical simplicity and insensitiveness to the number of reference labelled spectra, have been increasingly used for material mapping by labelling reflectance spectra in hyperspectral data labelling. For a particular hyperspectral data set, the accuracy of spectral labelling depends considerably upon the degree of unambiguous spectral matching achieved by the spectral similarity metric used. In this work, we propose a new methodology for quantifying spectral similarity for hyperspectral data labelling for surface materials identification. Developed adopting the multiple classifier system architecture, the proposed methodology unifies into a single framework the differential performances of eight different spectral similarity metrics for the quantification of spectral matching for surface materials. The proposed methodology has been implemented on two types of hyperspectral data viz. image (airborne hyperspectral images) and non-image (library spectra) for numerous surface materials identification. Further, the performance of the proposed methodology has been compared with the support vector machines (SVM) approach, and with all the base spectral similarity metrics. The results indicate that, for the hyperspectral images, the performance of the proposed methodology is comparable with that of the SVM. For the library spectra, the proposed methodology shows a consistently higher (increase of about 30% when compared to SVM) classification accuracy. The proposed methodology has the potential to serve as a general library search method for materials identification using hyperspectral data. 相似文献
Marine oil spills have caused major threats to marine environment over the past few years.The early detection of the oil spill is of great significance for the prevention and control of marine disasters.At present,remote sensing is one of the major approaches for monitoring the oil spill.Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy(H) and reflection entropy(A).The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill.The findings show that the information extracted from(1–A)×(1–H) and(1–H)×A parameters is relatively evident effects.The results of extraction of the oil spill information based on H×A parameter are relatively not good.The combination of the two has something to do with H and A values.In general,when H0.7,A value is relatively small.Here,the extraction of the oil spill information using(1–A)×(1–H) and(1–H)×A parameters obtains evident effects.Whichever combined parameter is adopted,oil well data would cause certain false alarm to the extraction of the oil spill information.In particular the false alarm of the extracted oil spill information based on(1–A)×(1–H) is relatively high,while the false alarm based on(1–A)×H and(1–H)×A parameters is relatively small,but an image noise is relatively big.The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature. 相似文献