This thesis explores how to effectively model trust in the environment of mobile vehicular ad-hoc networks. We consider each vehicle’s travel path planning to be guided by an intelligent agent that receives traffic reports from other agents in the environment. Determining the trustworthiness of these reports is thus a critical task. We take as a starting point the multi-dimensional trust model of Minhas et al. That work had a two-phased approach: i) model trust and ii) execute an algorithm for using that trust modeling, when deciding what route to take. The framework presented in this thesis aims to clarify i) the messaging that should be supported, ii) the internal representation of the messaging and the trust information and iii) the algorithms for sending and receiving information (thus updating knowledge) in order to perform decision making during route planning. A significant contribution is therefore offered through clarification and extension of the original trust modeling approach. In addition we design a comprehensive, extensive simulation testbed that is used to validate the effectiveness and robustness of the model. This testbed supports a variety of metrics and is able to perform testing in environments with a large number of cars. This constitutes the second significant contribution of the thesis. Overall, we present a valuable model for knowledge management in mobile vehicular ad-hoc networks through a combination of trust modeling, ontological representation of concepts and facts, and a methodology for discovering and updating user models. Included is a representation and implementation of both a push-based and pull-based messaging protocol. We also demonstrate the effectiveness of this model through validation conducted using our simulation testbed, focusing first on a subset of the multi-faceted trust model in order to highlight the value of the underlying representation, decision making algorithm and simulation metrics. One very valuable result is a demonstration of the importance of the combined use of the different dimensions employed in the trust modeling.

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.

Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)

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.

Thesis (Ph D.)--University of Technology Sydney, 2007.

Thesis (M.B.A.)--Carleton University, 2001. / Includes bibliographical references (p. 108-116). Also available in electronic format on the Internet.

Thesis (M.App.Sc.) - Carleton University, 2002. / Includes bibliographical references (p. 128-131). Also available in electronic format on the Internet.

Thesis (M.A.)--Carleton University, 2002. / Includes bibliographical references (p. 121-144). Also available in electronic format on the Internet.