Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 89-91).

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 82-89).

Not available / text

Not available / text

No description available.

No description available.

The area of Agent Technology is a relatively new area and one that has generated great interest within computing circles. The concept of software identities that have the intelligence to perform some of the tasks that humans perform has great potential. Such software identities are slowly making their way into many complex systems ranging from air traffic control systems to web search engines, and removing the direct need for the 'human in the loop'. This type of software that has the ability or intelligence to perform some of the tasks that would otherwise be performed by humans has been coined the term 'Intelligent Agents'. / In many of these agent based systems there may be a series of agents that are operating together to achieve common goals. Such systems are often referred to as a multiagent system. Agents within a multiagent system, usually share information and knowledge between each other in a collaborative manner to achieve a common goal. The information that is shared around is normally learned knowledge. / For the study conducted for this Master's project, collaborative agent learning is examined. The main emphasis of this study is to research how agents can learn. This learned knowledge can then be shared amongst other agents in a system to achieve a common goal. To investigate this, a case study was developed, where the aim was to develop an Automatic Target Recognition (ATR) system containing two main types of agents- a Detection Agent and a Recognition Agent. These agents would have specific roles within the system; however both would be trained to identify the same target images. These agents would then collaborate with each other to positively identify these target images when presented with masses of data in the form of test images as input into the ATR system. / The intelligent and learning aspects of these agents are implemented using Artificial Neural Networks. Two different Artificial Neural Network techniques are used for the two agents. This thesis contains a detailed discussion on Agent Technology, Artificial Neural Networks, the ATR system and descriptions of how the Detection and Recognition Agents were developed and tested. Experimental results are also presented with a discussion on the overall success of the ATR system, the results obtained, problems encountered and future directives for this research. / Thesis (MEng(ElectronicsEngineering))--University of South Australia, 2005.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Also available online.

Thesis (M.S.)--Ohio University, 2002. / Title from PDF t.p.

Based on doctoral research, 1996-2000--Rensselaer Polytechnic Institute. / Description based on print version record. Includes bibliographical references (p. 159-168) and index.