Trust-based social mechanism to counter deceptive behaviour
Lim Choi Keung, Sarah Niukyun (2011) Trust-based social mechanism to counter deceptive behaviour. PhD thesis, University of Warwick.
WRAP_THESIS_Lim-Choi-Keung_2011.pdf - Submitted Version - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://webcat.warwick.ac.uk/record=b2533303~S1
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.
|Item Type:||Thesis or Dissertation (PhD)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Library of Congress Subject Headings (LCSH):||Intelligent agents (Computer software)|
|Institution:||University of Warwick|
|Theses Department:||Department of Computer Science|
|Sponsors:||University of Warwick. Dept. of Computer Science|
|Extent:||xx, 286 leaves : ill., charts|
Actions (login required)