Introducing fuzzy trust for managing belief conflict over semantic web data

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model

Planning and Doing Things

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I was interested in computers by the age of 15 and gave talks on them at school. I attended evening classes a couple of years later while still at school travelling on the bus for an hour in the evening to a college in Leeds to learn programming (in COBOL!). Computers at that time filled a room, you submitted your exercises on punched card and got the results the following day. I built my first AI planner over 35 years ago. I’d already been on an early AI course at Lancaster University where the language of choice for teaching a range of topics was POP-2 and wanted to do a Summer project to create a problem solver. With support from Donald Michie and his team at Edinburgh I tried to create a Graph Traverser along the lines they were working on. Boy, am I glad I got involved with Computers, AI and planning technology

Improving trust and reputation assessment with dynamic behaviour

Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time. Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour. In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change

A fuzzy approach to reasoning with trust, distrust and insufficient trust

Multi-agent systems are based upon cooperative interactions between agents, in which agents provide information, resources and services to others. Typically agents are autonomous and self-interested, meaning that they have control over their own actions, and that they seek to maximise their own goal achievement, rather than necessarily acting in a benevolent or socially-oriented manner. Consequently, interaction outcomes are uncertain since commitments can be broken and the actual services rendered may differ from expectations in terms of cost or quality. Cooperation is, therefore, an uncertain interaction, that has an inherent risk of failure or reduced performance. In this paper we show how agents can use trust to manage this risk. Our approach uses fuzzy logic to represent trust and allow agents to reason with uncertain and imprecise information regarding others' trustworthiness

A Fuzzy Approach to Reasoning with Trust, Distrust and Insufficient Trust

Abstract. Multi-agent systems are based upon cooperative interactions between agents, in which agents provide information, resources and services to others. Typically agents are autonomous and self-interested, meaning that they have control over their own actions, and that they seek to maximise their own goal achievement, rather than necessarily acting in a benevolent or socially-oriented manner. Consequently, interaction outcomes are uncertain since commitments can be broken and the actual services rendered may differ from expectations in terms of cost or quality. Cooperation is, therefore, an uncertain interaction, that has an inherent risk of failure or reduced performance. In this paper we show how agents can use trust to manage this risk. Our approach uses fuzzy logic to represent trust and allow agents to reason with uncertain and imprecise information regarding others ’ trustworthiness.

A model for smart factories in the pharmaceutical manufacturing sector

Since the turn of the century, the manufacturing industry has metamorphosed from manually driven systems to digitalisation. Product life cycles have shortened and customer demands have become more intense. Globalisation has brought about challenges that drive the need for smart manufacturing. Industry 4.0 has emerged as a response to these demands. The integration of various processes, facilities and systems throughout the value chain and digitalisation of physical systems is promoted in Industry 4.0. Due to increased competitive pressures, organisations are strategically looking at automation to deliver competitive advantage in delivering products at the right cost, quality, time and volumes to the customers. Organisations are therefore looking for manufacturing solutions that are technology driven, such as cyber-physical systems, big data, collaborative robots and the Internet of Things. This allows autonomous communication throughout the value chain between machine-to-machine and human-to-machine. The smart factory, a component of Industry 4.0, is a self-organised, modular, highly flexible and reconfigurable factory that enables the production of customised products at low cost, therefore maximising profitability. Smart manufacturing can bring about competitive advantages for an organisation. Labour concerns have been raised against automation and smart manufacturing, citing potential job losses, workforce redundancy and potential employee lay-offs. This unease, in turn, influences the employees’ attitude towards technology, which could lead either to its acceptance or refusal. The purpose of this research is to enhance the understanding of smart factories in the pharmaceutical industry by conducting a systematic analysis of the factors which influence the attitude of those involved towards a smart factory implementation. This study focuses on the perceptions among employees and management. The research is a quantitative study consisting of a literature review of the key concepts related to Industry 4.0, smart factories and technology-acceptance theories. The empirical study consisted of surveys completed by management and employees of one of the pharmaceutical manufacturers in South Africa. The questionnaire used in this research consists of questions regarding demographic data and questions regarding the perception of change and factors influencing attitudes towards the acceptance of technology, within the pharmaceutical manufacturing company. Descriptive statistics were used to summarise the data into a more condensed form, which could simplify the identification of patterns in the data. Inferential statistics were used to validate if the conclusions made from the sample data could be inferred to a larger population. Various factors influence perceptions about ease of use and usefulness, which then, in turn, influence attitudes and the intention to use technology. These factors have been examined by numerous authors in the technology acceptance literature. Recommended factors based on the statistical analysis of the questionnaire results were identified. A model, supported by Exploratory Factor Analysis, Correlations and ANOVA Testing identified the following factors as having an influence on the Attitude towards the Positive Impact of Smart Factories, within the pharmaceutical manufacturing company: Training and Development, Individual Characteristics, Trust, Organisational Culture, Resources and Costs and Job Security. The importance of each factor was identified to understand its function how to improve the implementation of smart factories. The research results indicated that the perception of management and employees is different on factors like such as Training, Individual Characteristics, Trust, Resources and Costs, Automation and Support and Parent Company in relation to technology acceptance. There was however no difference in perception between managers and employees on Security, Government Laws and Regulations, Organisational Culture, Peer Support and Organisational Support in relation to technology acceptance. The research study contributed to the identification and understanding of the factors influencing the implementation of smart factories in the pharmaceutical industry

Trust-based social mechanism to counter deceptive behaviour

The actions of an autonomous agent are driven by its individual goals and its knowledge and beliefs about its environment. As agents can be assumed to be selfinterested, they strive to achieve their own interests and therefore their behaviour can sometimes be difficult to predict. However, some behaviour trends can be observed and used to predict the future behaviour of agents, based on their past behaviour. This is useful for agents to minimise the uncertainty of interactions and ensure more successful transactions. Furthermore, uncertainty can originate from malicious behaviour, in the form of collusion, for example. Agents need to be able to cope with this to maximise their benefits and reduce poor interactions with collusive agents. This thesis provides a mechanism to support countering deceptive behaviour by enabling agents to model their agent environment, as well as their trust in the agents they interact with, while using the data they already gather during routine agent interactions. As agents interact with one another to achieve the goals they cannot achieve alone, they gather information for modelling the trust and reputation of interaction partners. The main aim of our trust and reputation model is to enable agents to select the most trustworthy partners to ensure successful transactions, while gathering a rich set of interaction and recommendation information. This rich set of information can be used for modelling the agents' social networks. Decentralised systems allow agents to control and manage their own actions, but this suffers from limiting the agents' view to only local interactions. However, the representation of the social networks helps extend an agent's view and thus extract valuable information from its environment. This thesis presents how agents can build such a model of their agent networks and use it to extract information for analysis on the issue of collusion detection.EThOS - Electronic Theses Online ServiceUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo

Αυτόματες Διαπραγματεύσεις Υπολογιστικά Νοημόνων Οντοτήτων σε Ηλεκτρονικές Αγορές

Οι αυτόματες διαπραγματεύσεις που διεξάγονται στα πλαίσια των Ηλεκτρονικών Αγορών αποτελούν ερευνητικό αντικείμενο με αρκετό ενδιαφέρον τα τελευταία χρόνια. Το σενάριο που απεικονίζει πραγματικές καταστάσεις υποδεικνύει πως οι οντότητες λειτουργούν κάτω από πλήρη άγνοια για τα χαρακτηριστικά των υπολοίπων. Αυτό σημαίνει πως οι συμπεριφορές που πρόκειται να αναπτυχθούν πρέπει να ενσωματώνουν μηχανισμούς διαχείρισης της αβεβαιότητας που δημιουργεί η άγνοια αυτή αλλά και έξυπνες μεθόδους για τη μοντελοποίηση της κάθε πτυχής του εξεταζόμενου σεναρίου. Στην παρούσα διατριβή υιοθετούμε τεχνικές υπολογιστικής νοημοσύνης ώστε να προτείνουμε αποδοτικούς μηχανισμούς καθορισμού της συμπεριφοράς των οντοτήτων που συμμετέχουν σε διαπραγματεύσεις. Καλύπτουμε όλο το φάσμα μιας αγοράς προτείνοντας μεθόδους για τον καθορισμό των βασικών παραμέτρων αλλά και μοντέλα λήψης αποφάσεων σε κάθε γύρο των διαπραγματεύσεων. Λαμβάνουμε υπόψιν μας την αβεβαιότητα στις ενέργειες των οντοτήτων ταυτόχρονα με το στόχο της μεγιστοποίησης του επιδιωκόμενου κέρδους. Προτείνουμε μοντέλα λήψης απόφασης τα οποία βασίζονται σε διαφορετικές πτυχές του σεναρίου μιας διαπραγμάτευσης ώστε να αναδείξουμε ποιο από αυτά είναι το βέλτιστο για να υιοθετηθεί. Μελετήσαμε τη συμπεριφορά των αγοραστών όπως επίσης και των πωλητών. Οι προτεινόμενοι μηχανισμοί λήψης αποφάσεων για κάθε ένα από τους δύο βασίζονται στην Ασαφή Λογική, τη Θεωρία Παιγνίων, τη Θεωρία του Σμήνους και τη Θεωρία Βέλτιστης παύσης.Automated negotiations consist an interesting research domain for many years. A scenario, mostly depicting real life negotiations, defines that entities act under no knowledge on the characteristics of the rest of them. This means that their behavior should incorporate mechanisms for handling uncertainty created by the lack of knowledge as well as intelligent methods for modelling every aspect of the discussed scenario. In this PhD Thesis, we adopt computational intelligence techniques in order to propose efficient mechanisms for the definition of the behavior of entities participating in Electronic Markets. We cover the entire framework defined in a marketplace by proposing methodologies for the definition of basic parameters together with decision making models at every step of each negotiation. We take into consideration the uncertainty in such scenarios together with profit maximization. We propose decision making models that are based on different aspects of the discussed scenario in order to reveal the optimal one. We study buyers’ as well as sellers’ behavior. The proposed decision making mechanisms, for every part (buyers and sellers), are based on Fuzzy Logic, Game Theory, Swarm Intelligence and Optimal Stopping Theory