Lim Choi Keung, Sarah Niukyun (2011) Trust-based social mechanism to counter deceptive behaviour. PhD thesis, University of Warwick.

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Abstract

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)
Official Date:	June 2011
Dates:	Date Event June 2011 Submitted
Institution:	University of Warwick
Theses Department:	Department of Computer Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Griffiths, Nathan
Sponsors:	University of Warwick. Dept. of Computer Science
Extent:	xx, 286 leaves : ill., charts
Language:	eng
URI:	http://wrap.warwick.ac.uk/id/eprint/38163

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