Supply chain optimization towards personalizing web services

Personalization, which has the ultimate goal of satisfying user’s requests, can be perceived in terms of QoS measurement. As one of the means for the success of Semantics Web, many techniques have been effectively used in modeling and developing web service personalization. However, most of these methodologies relied heavily on detailed implicit and explicit information supply by users during initial and subsequent interactions with the systems. We propose in this paper a novel approach using the supply chain management (SCM) technique in personalizing web services as against the conventional notion of applying SCM only to product manufacturing. Our user-model based framework uses multi-agent system (MAS) components in taking requests from users and working towards their satisfaction including seeking for additional information outside the system as the need arises. Only basic stereotype information furnished by potential users at initial contact is required for personalization during subsequent interactions with the system. The system is adaptive and aimed at high quality autonomous information services where users are successfully presented preferred web services with minimum information request

Obstacle Avoidance System for Autonomous Transportation Vehicle based on Image Processing

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a Technical Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 4 (2002): E. Morimoto, M. Suguri and M. Umeda. Obstacle Avoidance System for Autonomous Transportation Vehicle based on Image Processing. Vol. IV. December 2002

Scalable fault tolerant Agent Grooming Environment - SAGE

Scalable fault tolerant Agent Grooming Environment (SAGE) is first open source initiative in South-Asia. It is a multi-agent system which has been developed according to FIPA (Foundation for Intelligent Physical Agents) 2002 specifications. SAGE has been designed with a distributed and decentralized architecture to achieve fault tolerance and scalability as its key features. Due to these characteristics, SAGE is not only regarded as 2nd generation Multi Agent System but also provides a competitive edge over other platforms.</p

A distributed services based conference planner application using software agents, grid services and web services

This demonstration highlights the applications of our research work i.e. second generation (Scalable Fault Tolerant Agent Grooming Environment - SAGE) Multi Agent System, Integration of Software Agents and Grid Computing and Autonomous Agent Architecture in the Agent Platform. It is a conference planner application that uses collaborative effort of services deployed geographically wide in different technologies i.e. Software Agents, Grid computing and Web services to perform useful tasks as required. Copyright 2005 ACM

Scalable fault tolerant agent grooming environment-SAGE

Multi-agent systems (MAS) advocate an agent-based approach to software engineering based on decomposing problems in terms of decentralized, autonomous agents that can engage in flexible, high-level interactions. This chapter introduces scalable fault tolerant agent grooming environment (SAGE), a second-generation Foundation for Intelligent Physical Agents (FIPA)-compliant multi-agent system developed at NIIT-Comtec, which provides an environment for creating distributed, intelligent, and autonomous entities that are encapsulated as agents. The chapter focuses on the highlight of SAGE, which is its decentralized fault-tolerant architecture that can be used to develop applications in a number of areas such as e-health, e-government, and e-science. In addition, SAGE architecture provides tools for runtime agent management, directory facilitation, monitoring, and editing messages exchange between agents. SAGE also provides a built-in mechanism to program agent behavior and their capabilities with the help of its autonomous agent architecture, which is the other major highlight of this chapter. The authors believe that the market for agent-based applications is growing rapidly, and SAGE can play a crucial role for future intelligent applications development. © 2007, IGI Global

Programming Verifiable Heterogeneous Agent Systems ⋆

Abstract. Our overall aim is to provide a verification framework for practical multi-agent systems. To achieve practicality, we must be able to describe and implement heterogeneous multi-agent systems. To achieve verifiability, we must define semantics appropriately for use in formal verification. Thus, in this paper, we tackle the problem of implementing heterogeneous multi-agent systems in a semantically clear, and appropriate, way.

Integrating remote sensing and GIS for prediction of rice protein contents

In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI