M.Sc. / Advancements in technology used in financial markets have led to substantial automation of tasks within the financial industry. Data analysis, trade execution and trade processing have been automated, reducing costs and increasing productivity. Algorithmic trading is the automated execution of trades on an electronic trading platform; it has been used to gain competitive advantage in financial markets since the early 1990s. Algorithmic trading applications, which must analyse information and determine whether to buy or sell, are well suited to the use of autonomous software agents. Multi-agent systems are better suited to the increasing complexity of algorithmic trading systems and the flexibility required by rapidly changing markets than single-agent systems. The granularity of components (agents) in multi-agent systems also promotes reuse and simplifies individual agent design. Algorithmic trading is, however, subject to challenges specifically in terms of data volume, speed of access and speed of processing. In order to utilise a multi-agent system solution the interactions between agents which allow distributed problem solving must be as efficient as possible. This dissertation investigates the use of indirect coordination to improve the efficiency of interactions between agents in multi-agent systems and to simplify agent design. Indirect coordination utilises environment abstractions known as artefacts to facilitate interaction between agents; such interaction can be simple data transfer or requests, complex coordination protocols as well as negotiation protocols. The investigation resulted in a framework that allows agents to transition between direct and indirect interaction techniques based on the specific interaction task at hand. The framework is built on two existing platforms, ii Java Agent DEvelopment Framework (JADE) and Common ARTifact Infrastructure for AGents Open environments (CARTAGO). These platforms are combined into the JADE-CARTAGO Algorithmic Trading (JCAT) framework that provides the infrastructure needed for both direct and indirect interactions. Investigations into the performance of the JCAT framework have shown that artefacts improve interaction efficiency by reducing data loss in tasks such as information publishing, and perform as well as direct communication within certain constraints for other tasks. When limiting the number of agents in an interaction to 50 agents, artefacts perform at least as well as direct communication using agent communication language messages.

Machine condition monitoring is gaining importance in industry due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often common that the data becomes available in small batches over a period of time. Hence, it is important to build a system that is able to accommodate new data as it becomes available without compromising the performance of the previously learned data. In real-world applications, more than one condition monitoring technology is used to monitor the condition of a machine. This leads to large amounts of data, which require a highly skilled diagnostic specialist to analyze. In this thesis, artificial intelligence (AI) techniques are used to build a condition monitoring system that has incremental learning capabilities. Two incremental learning algorithms are implemented, the first method uses Fuzzy ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. In addition, intelligent agents and multi-agent systems are used to build a condition monitoring system that is able to accommodate various analysis techniques. Experimentation was performed on two sets of condition monitoring data; the dissolved gas analysis (DGA) data obtained from high voltage bushings and the vibration data obtained from motor bearing. Results show that both Learn++ and FAM are able to accommodate new data without compromising the performance of classifiers on previously learned information. Results also show that intelligent agent and multi-agent system are able to achieve modularity and flexibility.

Yes / Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems. / The reported research has been part of the EU FP7 research project “PRIME”

No / This intention of this chapter is to critically examine uses of the World Wide Web by fans of cult movies. It begins by outlining how cult movies are categorised, and notes the problems that this engenders. Then the relationship between technologies and subcultural practices is observed. Examples are presented to illustrate the question of whether, through remediation processes, such practices tell us anything new about forms of contemporary communication and consumption.

This thesis undertakes to provide an architecture and understanding of the incorporation of trust into the paradigm of mobile multi-agent systems. Trust deliberation is a soft security approach to the problem of mobile agent security whereby an agent is protected from the malicious behaviour of others within the system. Using a trust approach capitalises on observing malicious behaviour rather than preventing it. We adopt an architectural approach to trust such than we do not provide a model in itself, numerous mathematical models for the calculation of trust based on a history of observations already exist. Rather we look to provide the framework enabling such models to be utilised by mobile agents. As trust is subjective we envisage a system whereby individual agents will use different trust models or different weighting mechanisms. Three architectures are provided. Centralised whereby the platform itself provides all of the services needed by an agent to make observations and calculate trust. Decentralised in which each individual agent is responsible for making observations, communicating trust and the calculation of its own trust in others. A hybrid architecture such that trust mechanisms are provided by the platform and additionally are embedded within the agents themselves. As an optimisation of the architectures proposed in this thesis, we introduce the notion of trust communities. A community is used as a means to represent the trust information in categorisations dependant upon various properties. Optimisation occurs in two ways; firstly with subjective communities and secondly with system communities. A customised implementation framework of the architectures is introduced in the form of our TEMPLE (Trust Enabled Mobile-agent PLatform Environment) and stands as the underpinning of a case-study implementation in order to provide empirical evidence in the form of scenario test-bed data as to the effectiveness of each architecture. The case study chosen for use in a trust based system is that of a fish market' as given the number of interactions, entities, and migration of agents involved in the system thus, providing substantial output data based upon the trust decisions made by agents. Hence, a good indicator of the effectiveness of equipping agents with trust ability using our architectures.

The electric power systems in U.S. Navy ships supply energy to sophisticated systems for weapons, communications, navigation and operation. The reliability and survivability of the Shipboard Power System (SPS) are critical to the mission of a surface combatant ship, especially under battle conditions. In the event of battle, various weapons might attack a ship. When a weapon hits the ship it can cause severe damage to the electrical system on the ship. This damage can lead to de-energization of critical loads on a ship that can eventually decrease a ship’s ability to survive the attack. It is very important, therefore, to maintain availability of energy to the connected loads that keep the power systems operational. Technology exists that enables the detection of an incoming weapon and prediction of the geographic area where the incoming weapon will hit the ship. This information can then be used to take reconfiguration actions before the actual hit so that the actual damage caused by the weapon hit is reduced. The Power System Automation Lab (PSAL) has proposed a unique concept called "Predictive Reconfiguration" which refers to performing reconfiguration of a ship’s power system before a weapon hit to reduce the potential damage to the electrical system caused by the impending weapon hit. The concept also includes reconfiguring the electrical system to restore power to as much of the healthy system as possible after the weapon hit. This dissertation presents a new methodology for Predictive Reconfiguration of a Shipboard Power System (SPS). This probabilistic approach includes a method to assess the damage that will be caused by a weapon hit. This method calculates the expected probability of damage for each electrical component on the ship. Also a heuristic method is included, which uses the expected probability of damage to determine reconfiguration steps to reconfigure the ship’s electrical network to reduce the damage caused by a weapon hit. This dissertation also presents a modified approach for performing a reconfiguration for restoration after the weapon hits the system. In this modified approach, an expert system based restoration method restores power to loads de-energized due to the weapon hit. These de-energized loads are restored in a priority order. The methods were implemented using multi-agent technology. A test SPS model based on the electrical layout of a non-nuclear surface combatant ship was presented. Complex scenarios representing electrical casualties caused due to a weapon hit, on the test SPS model, were presented. The results of the Predictive Reconfiguration methodology for complex scenarios were presented to illustrate the effectiveness of the developed methodology.

In order to succeed, it is imperative that all e-commerce systems include an effective and reliable trust and reputation modeling system. This is particularly true of decentralized e-commerce systems in which autonomous software engage in commercial transactions. Many researchers have sought to overcome the complexities of modeling a subjective, human concept like trust, resulting in several trust and reputation models. While these models each present a unique offering and solution to the problem, several issues persist. Most of the models require direct experience in the e-commerce system in order to make effective trust decisions. This leaves new agents and agents who only casually use the e-commerce system vulnerable. Additionally, the reputation ratings of agents who are relatively new to the system are often indistinguishable from scores for poorly performing agents. Finally, more tactics to defend against agents who exploit the characteristics of the open, distributed system for their own malicious needs are required. To address these issues, a new metric is devised and presented: seniority. Based on agent age and activity level within the e-commerce system, seniority provides a means of judging the credibility of other agents with little or no prior experience in the system. As the results of experimental analysis reveals, employing a reputation model that uses seniority provides considerable value to agents who are new agents, casual buyer agents and all other purchasing agents in the e-commerce system. This new metric therefore offers a significant contribution toward the development of enhanced and new trust and reputation models for deployment in real-world distributed e-commerce environments.

In order to succeed, it is imperative that all e-commerce systems include an effective and reliable trust and reputation modeling system. This is particularly true of decentralized e-commerce systems in which autonomous software engage in commercial transactions. Many researchers have sought to overcome the complexities of modeling a subjective, human concept like trust, resulting in several trust and reputation models. While these models each present a unique offering and solution to the problem, several issues persist. Most of the models require direct experience in the e-commerce system in order to make effective trust decisions. This leaves new agents and agents who only casually use the e-commerce system vulnerable. Additionally, the reputation ratings of agents who are relatively new to the system are often indistinguishable from scores for poorly performing agents. Finally, more tactics to defend against agents who exploit the characteristics of the open, distributed system for their own malicious needs are required. To address these issues, a new metric is devised and presented: seniority. Based on agent age and activity level within the e-commerce system, seniority provides a means of judging the credibility of other agents with little or no prior experience in the system. As the results of experimental analysis reveals, employing a reputation model that uses seniority provides considerable value to agents who are new agents, casual buyer agents and all other purchasing agents in the e-commerce system. This new metric therefore offers a significant contribution toward the development of enhanced and new trust and reputation models for deployment in real-world distributed e-commerce environments.

The electric power systems in U.S. Navy ships supply energy to sophisticated systems for weapons, communications, navigation and operation. The reliability and survivability of the Shipboard Power System (SPS) are critical to the mission of a surface combatant ship, especially under battle conditions. In the event of battle, various weapons might attack a ship. When a weapon hits the ship it can cause severe damage to the electrical system on the ship. This damage can lead to de-energization of critical loads on a ship that can eventually decrease a ship’s ability to survive the attack. It is very important, therefore, to maintain availability of energy to the connected loads that keep the power systems operational. Technology exists that enables the detection of an incoming weapon and prediction of the geographic area where the incoming weapon will hit the ship. This information can then be used to take reconfiguration actions before the actual hit so that the actual damage caused by the weapon hit is reduced. The Power System Automation Lab (PSAL) has proposed a unique concept called "Predictive Reconfiguration" which refers to performing reconfiguration of a ship’s power system before a weapon hit to reduce the potential damage to the electrical system caused by the impending weapon hit. The concept also includes reconfiguring the electrical system to restore power to as much of the healthy system as possible after the weapon hit. This dissertation presents a new methodology for Predictive Reconfiguration of a Shipboard Power System (SPS). This probabilistic approach includes a method to assess the damage that will be caused by a weapon hit. This method calculates the expected probability of damage for each electrical component on the ship. Also a heuristic method is included, which uses the expected probability of damage to determine reconfiguration steps to reconfigure the ship’s electrical network to reduce the damage caused by a weapon hit. This dissertation also presents a modified approach for performing a reconfiguration for restoration after the weapon hits the system. In this modified approach, an expert system based restoration method restores power to loads de-energized due to the weapon hit. These de-energized loads are restored in a priority order. The methods were implemented using multi-agent technology. A test SPS model based on the electrical layout of a non-nuclear surface combatant ship was presented. Complex scenarios representing electrical casualties caused due to a weapon hit, on the test SPS model, were presented. The results of the Predictive Reconfiguration methodology for complex scenarios were presented to illustrate the effectiveness of the developed methodology.

In order to succeed, it is imperative that all e-commerce systems include an effective and reliable trust and reputation modeling system. This is particularly true of decentralized e-commerce systems in which autonomous software engage in commercial transactions. Many researchers have sought to overcome the complexities of modeling a subjective, human concept like trust, resulting in several trust and reputation models. While these models each present a unique offering and solution to the problem, several issues persist. Most of the models require direct experience in the e-commerce system in order to make effective trust decisions. This leaves new agents and agents who only casually use the e-commerce system vulnerable. Additionally, the reputation ratings of agents who are relatively new to the system are often indistinguishable from scores for poorly performing agents. Finally, more tactics to defend against agents who exploit the characteristics of the open, distributed system for their own malicious needs are required. To address these issues, a new metric is devised and presented: seniority. Based on agent age and activity level within the e-commerce system, seniority provides a means of judging the credibility of other agents with little or no prior experience in the system. As the results of experimental analysis reveals, employing a reputation model that uses seniority provides considerable value to agents who are new agents, casual buyer agents and all other purchasing agents in the e-commerce system. This new metric therefore offers a significant contribution toward the development of enhanced and new trust and reputation models for deployment in real-world distributed e-commerce environments.