In this paper, we present an agent-based decision-supporting system for Taguchi experiment planning. Among the four major parts of Taguchi experiment, the planning phase includes the most important decision-making issues such as determination of experi ment objectives, quality characteristics, and control factors. The planning phase, however, has not been paid proper attention by experi ment designers. We have developed ADTEP (Agent-based Decision-supporting system for Taguchi Experiment Planning) to facilitate the planning tasks of experiment designer. ADTEP is composed of two agent-based mechanisms. The first employs an Internet agent that collects the domain knowledge from knowledge providers who may be distributed in remote places. Another agent then visualizes the collected knowledge and reports it to the experiment designer. Engineers who would normally have difficulties in collaborating because of limitations on their time or because they are in different places can easily work together in the same experiment team and brainstorm to make good decisions. The second agent-based mechanism offers context-sensitive advice generated by another intelligent agent dur ing the experiment planning process. It prevents the experiment designer from making mistakes, which will increase the feasibility of the experiment and minimize the unnecessary expense of time and resources.

Although the development of Global Positioning System (GPS) are more and more mature, its accuracy is just acceptable for outdoor positioning, not positioning for the indoor of building and the underpass. For the positioning application area for the indoor of building and the underpass, GPS even cannot achieve that accuracy because of the construction materials while the requirement for accurate positioning in the indoor of building and the underpass, because a space, a person is necessary, may be very small space with several square meters in the indoor of building and the underpass. The Received Signal Strength (RSS) based localization is becoming a good choice especially for the indoor of building and the underpass scenarios where the WiFi signals of IEEE 802.11, Wireless LAN, are available in almost every indoor of building and the underpass. The fundamental requirement of such localization system is to estimate location from Access Point (AP) to mobile device using RSS at a specific location. The Multi-path fading effects in this process make RSS to fluctuate unpredictably, causing uncertainty in localization. To deal with this problem, the combination for the method of Neural Networks and Push-Pull Estimation is applied so that the carried along the devices can learn and make the decision of position using mobile device where it is in the indoor of building and the underpass.