Using Agents for Mining Maintenance Data while interacting in 3D Objectoriented Virtual Environments

This report demonstrates the development of: (a) object-oriented representation to provide 3D interactive environment using data provided by Woods Bagot; (b) establishing basis of agent technology for mining building maintenance data, and (C) 3D interaction in virtual environments using object-oriented representation. Applying data mining over industry maintenance database has been demonstrated in the previous report.

Developing Software Agents for Data Mining : [AIMM] Agents for Improving Maintenance Management

This report demonstrates the development of: • Development of software agents for data mining • Link data mining to building model in virtual environments • Link knowledge development with building model in virtual environments • Demonstration of software agents for data mining • Populate with maintenance data

Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

Corneal sensitivity and slit scanning in vivo confocal microscopy of the subbasal nerve plexus of the normal central and peripheral human cornea

Purpose: To determine the subbasal nerve density and tortuosity at 5 corneal locations and to investigate whether these microstructural observations correlate with corneal sensitivity. Method: Sixty eyes of 60 normal human subjects were recruited into 1 of 3 age groups, group 1: aged ,35 years, group 2: aged 35–50 years, and group 3: aged .50 years. All eyes were examined using slit-lamp biomicroscopy, noncontact corneal esthesiometry, and slit scanning in vivo confocal microscopy. Results: The mean subbasal nerve density and the mean corneal sensitivity were greatest centrally (14,731 6 6056 mm/mm2 and 0.38 6 0.21 millibars, respectively) and lowest in the nasal mid periphery (7850 6 4947 mm/mm2 and 0.49 6 0.25 millibars, respectively). The mean subbasal nerve tortuosity coefficient was greatest in the temporal mid periphery (27.3 6 6.4) and lowest in the superior mid periphery (19.3 6 14.1). There was no significant difference in mean total subbasal nerve density between age groups. However, corneal sensation (P = 0.001) and subbasal nerve tortuosity (P = 0.004) demonstrated significant differences between age groups. Subbasal nerve density only showed significant correlations with corneal sensitivity threshold in the temporal cornea and with subbasal nerve tortuosity in the inferior and nasal cornea. However, these correlations were weak. Conclusions: This study quantitatively analyzes living human corneal nerve structure and an aspect of nerve function. There is no strong correlation between subbasal nerve density and corneal sensation. This study provides useful baseline data for the normal living human cornea at central and mid-peripheral locations

Knowledge discovery for adaptive negotiation agents in e-marketplaces

Intelligent software agents are promising in improving the effectiveness of e-marketplaces for e-commerce. Although a large amount of research has been conducted to develop negotiation protocols and mechanisms for e-marketplaces, existing negotiation mechanisms are weak in dealing with complex and dynamic negotiation spaces often found in e-commerce. This paper illustrates a novel knowledge discovery method and a probabilistic negotiation decision making mechanism to improve the performance of negotiation agents. Our preliminary experiments show that the probabilistic negotiation agents empowered by knowledge discovery mechanisms are more effective and efficient than the Pareto optimal negotiation agents in simulated e-marketplaces.