K x N Trust-Based Agent Reputation

In this research, a multi-agent system called KMAS is presented that models an environment of intelligent, autonomous, rational, and adaptive agents that reason about trust, and adapt trust based on experience. Agents reason and adapt using a modification of the k-Nearest Neighbor algorithm called (k X n) Nearest Neighbor where k neighbors recommend reputation values for trust during each of n interactions. Reputation allows a single agent to receive recommendations about the trustworthiness of others. One goal is to present a recommendation model of trust that outperforms MAS architectures relying solely on direct agent interaction. A second goal is to converge KMAS to an emergent system state where only successful cooperation is allowed. Three experiments are chosen to compare KMAS against a non-(k X n) MAS, and between different variations of KMAS execution. Research results show KMAS converges to the desired state, and in the context of this research, KMAS outperforms a direct interaction-based system

A Methodology to Evolve Cooperation in Pursuit Domain using Genetic Network Programming

The design of strategies to devise teamwork and cooperation among agents is a central research issue in the field of multi-agent systems (MAS). The complexity of the cooperative strategy design can rise rapidly with increasing number of agents and their behavioral sophistication. The field of cooperative multi-agent learning promises solutions to such problems by attempting to discover agent behaviors as well as suggesting new approaches by applying machine learning techniques. Due to the difficulty in specifying a priori for an effective algorithm for multiple interacting agents, and the inherent adaptability of artificially evolved agents, recently, the use of evolutionary computation as a machining learning technique and a design process has received much attention. In this thesis, we design a methodology using an evolutionary computation technique called Genetic Network Programming (GNP) to automatically evolve teamwork and cooperation among agents in the pursuit domain. Simulation results show that our proposed methodology was effective in evolving teamwork and cooperation among agents. Compared with Genetic Programming approaches, its performance is significantly superior, its computation cost is less and the learning speed is faster. We also provide some analytical results of the proposed approach

Combining MAS and P2P Systems: The Agent Trees Multi-Agent System (ATMAS)

The seamless retrieval of information distributed across networks has been one of the key goals of many systems. Early solutions involved the use of single static agents which would retrieve the unfiltered data and then process it. However, this was deemed costly and inefficient in terms of the bandwidth since complete files need to be downloaded when only a single value is often all that is required. As a result, mobile agents were developed to filter the data in situ before returning it to the user. However, mobile agents have their own associated problems, namely security and control. The Agent Trees Multi-Agent System (AT-MAS) has been developed to provide the remote processing and filtering capabilities but without the need for mobile code. It is implemented as a Peer to Peer (P2P) network of static intelligent cooperating agents, each of which control one or more data sources. This dissertation describes the two key technologies have directly influenced the design of ATMAS, Peer-to-Peer (P2P) systems and Multi-Agent Systems (MAS). P2P systems are conceptually simple, but limited in power, whereas MAS are significantly more complex but correspondingly more powerful. The resulting system exhibits the power of traditional MAS systems while retaining the simplicity of P2P systems. The dissertation describes the system in detail and analyses its performance

Combining MAS and P2P systems : the Agent Trees Multi-Agent System (ATMAS)

The seamless retrieval of information distributed across networks has been one of the key goals of many systems. Early solutions involved the use of single static agents which would retrieve the unfiltered data and then process it. However, this was deemed costly and inefficient in terms of the bandwidth since complete files need to be downloaded when only a single value is often all that is required. As a result, mobile agents were developed to filter the data in situ before returning it to the user. However, mobile agents have their own associated problems, namely security and control. The Agent Trees Multi-Agent System (AT-MAS) has been developed to provide the remote processing and filtering capabilities but without the need for mobile code. It is implemented as a Peer to Peer (P2P) network of static intelligent cooperating agents, each of which control one or more data sources. This dissertation describes the two key technologies have directly influenced the design of ATMAS, Peer-to-Peer (P2P) systems and Multi-Agent Systems (MAS). P2P systems are conceptually simple, but limited in power, whereas MAS are significantly more complex but correspondingly more powerful. The resulting system exhibits the power of traditional MAS systems while retaining the simplicity of P2P systems. The dissertation describes the system in detail and analyses its performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A framework for knowledge discovery within business intelligence for decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Vertrauen und Betrug in Multi-Agenten Systemen : Erweiterung des Vertrauensmodells von Castelfranchi und Falcone um eine Kommunikationskomponente

Diese Arbeit beschäftigt sich mit betrügerischen Agenten in Künstlichen Gesellschaften und damit, wie andere Agenten sich vor ihnen schützen können. Zu diesem Zweck werden Agenten mit Berechnungsmodellen für zwei Konzepte von "Vertrauen" ausgestattet. Zum einen berechnen sie Vertrauen in Interaktionspartner mit einer präzisierten Variante des Modells von Castelfranchi und Falcone. Zum anderen benutzen sie eine hier vorgestellte Form von Vertrauen, um mit anderen über das Verhalten von unbekannten Agenten zu kommunizieren. Durch diesen Datenaustausch sind sie in der Lage, fremde Agenten wesentlich schneller und besser einzuschätzen. Mit diesem Wissen können sich Agenten effektiver vor betrügerischen und nicht-benevolenten Agenten schützen. Das Vertrauen in Kommunikationspartner schafft einen "sozialen Kitt", über den innerhalb einer Gruppe Informationen zuverlässig ausgetauscht werden können. Desweiteren wird hier das Offen Gespielte Gefangenendilemma mit Partnerauswahl vorgestellt. Dabei handelt es sich um ein spieltheoretisches Modell, in dem Agenten andere betrügen können. Diese Variation des Gefangenendilemmas dient als Experimentalumgebung für heterogene Agentengesellschaften. Diese Experimentalumgebung besitzt wichtige Eigenschaften von Anwendungsszenarien wie z.B. die Kooperation in Virtuellen Märkten. Sie ist so gestaltet, dass die Effektivität von Strategien im Umgang mit betrügerischen Agenten untersucht werden kann. Dies bedeutet, dass mit ihrer Hilfe Turniere, ähnlich dem in der Literatur viel beachteten Turnier von Axelrod, durchgeführt werden können. Schließlich wird diese Experimentalumgebung genutzt, um das hier vorgestellte Modell des Vertrauens in Kommunikationspartner in einer Reihe von Experimenten, in denen die Agenten kein a priori Wissen über das Verhalten anderer haben, zu analysieren. Bei dieser Analyse werden Konfigurationen von verschieden ehrlichen und kooperationswilligen Agenten untersucht. In der Evaluation des Ansatzes zeigt sich, dass Agenten durch den Austausch von Wissen mit anderen vertrauenswürdigen Agenten ihre Interaktionspartner besser einschätzen können. Insbesondere sind sie in der Lage, Interaktionspartner einzuschätzen, die sie selbst noch nie beobachten konnten. In den untersuchten Agentengesellschaften bedeutet dies einen Performanzgewinn von mehr als fünfzehn Prozent, ohne dass die Agenten ein a priori Wissen über das Verhalten ihrer Interaktionspartner haben. Die Benutzung von Vertrauen und Kommunikation zahlt sich insbesondere dann aus, wenn nur wenige Beobachtungen über das Verhalten anderer zur Verfügung stehen

A framework for knowledge discovery within business intelligence for decision support

Business Intelligence (BI) techniques provide the potential to not only efficiently manage but further analyse and apply the collected information in an effective manner. Benefiting from research both within industry and academia, BI provides functionality for accessing, cleansing, transforming, analysing and reporting organisational datasets. This provides further opportunities for the data to be explored and assist organisations in the discovery of correlations, trends and patterns that exist hidden within the data. This hidden information can be employed to provide an insight into opportunities to make an organisation more competitive by allowing manager to make more informed decisions and as a result, corporate resources optimally utilised. This potential insight provides organisations with an unrivalled opportunity to remain abreast of market trends. Consequently, BI techniques provide significant opportunity for integration with Decision Support Systems (DSS). The gap which was identified within the current body of knowledge and motivated this research, revealed that currently no suitable framework for BI, which can be applied at a meta-level and is therefore tool, technology and domain independent, currently exists. To address the identified gap this study proposes a meta-level framework: - ‘KDDS-BI’, which can be applied at an abstract level and therefore structure a BI investigation, irrespective of the end user. KDDS-BI not only facilitates the selection of suitable techniques for BI investigations, reducing the reliance upon ad-hoc investigative approaches which rely upon ‘trial and error’, yet further integrates Knowledge Management (KM) principles to ensure the retention and transfer of knowledge due to a structured approach to provide DSS that are based upon the principles of BI. In order to evaluate and validate the framework, KDDS-BI has been investigated through three distinct case studies. First KDDS-BI facilitates the integration of BI within ‘Direct Marketing’ to provide innovative solutions for analysis based upon the most suitable BI technique. Secondly, KDDS-BI is investigated within sales promotion, to facilitate the selection of tools and techniques for more focused in store marketing campaigns and increase revenue through the discovery of hidden data, and finally, operations management is analysed within a highly dynamic and unstructured environment of the London Underground Ltd. network through unique a BI solution to organise and manage resources, thereby increasing the efficiency of business processes. The three case studies provide insight into not only how KDDS-BI provides structure to the integration of BI within business process, but additionally the opportunity to analyse the performance of KDDS-BI within three independent environments for distinct purposes provided structure through KDDS-BI thereby validating and corroborating the proposed framework and adding value to business processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Investigation of an emotional virtual human modelling method

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In order to simulate virtual humans more realistically and enable them life-like behaviours, several exploration research on emotion calculation, synthetic perception, and decision making process have been discussed. A series of sub-modules have been designed and simulation results have been presented with discussion. A visual based synthetic perception system has been proposed in this thesis, which allows virtual humans to detect the surrounding virtual environment through a collision-based synthetic vision system. It enables autonomous virtual humans to change their emotion states according to stimuli in real time. The synthetic perception system also allows virtual humans to remember limited information within their own First-in-first-out short-term virtual memory. The new emotion generation method includes a novel hierarchical emotion structure and a group of emotion calculation equations, which enables virtual humans to perform emotionally in real-time according to their internal and external factors. Emotion calculation equations used in this research were derived from psychologic emotion measurements. Virtual humans can utilise the information in virtual memory and emotion calculation equations to generate their own numerical emotion states within the hierarchical emotion structure. Those emotion states are important internal references for virtual humans to adopt appropriate behaviours and also key cues for their decision making. The work introduces a dynamic emotional motion database structure for virtual human modelling. When developing realistic virtual human behaviours, lots of subjects were motion-captured whilst performing emotional motions with or without intent. The captured motions were endowed to virtual characters and implemented in different virtual scenarios to help evoke and verify design ideas, possible consequences of simulation (such as fire evacuation). This work also introduced simple heuristics theory into decision making process in order to make the virtual human’s decision making more like real human. Emotion values are proposed as a group of the key cues for decision making under the simple heuristic structures. A data interface which connects the emotion calculation and the decision making structure together has also been designed for the simulation system