A Multi-Dimensional Trust Model for Heterogeneous Contract Observations
Reece, Steven, Rogers, Alex, Roberts, Stephen and Jennings, N. R. (2007) A Multi-Dimensional Trust Model for Heterogeneous Contract Observations. In, Twenty-Second AAAI Conference on Artificial Intelligence, Vancouver, Canada, , 128-135.
In this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dimensional contracts. We consider the specific case of an agent attempting to procure a bundle of services that are subject to correlated quality of service failures (e.g. due to use of shared resources or infrastructure), and where the direct experience of other agents within the system consists of contracts over different combinations of these services. To this end, we present a formalism based on the Kalman filter that represents trust as a vector estimate of the probability that each service will be successfully delivered, and a covariance matrix that describes the uncertainty and correlations between these probabilities. We describe how the agents’ direct experiences of contract outcomes can be represented and combined within this formalism, and we empirically demonstrate that our formalism provides significantly better trustworthiness estimates than the alternative of using separate single-dimensional trust models for each separate service (where information regarding the correlations between each estimate is lost).
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Event Dates: July 2007|
|Divisions :||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
|Accepted Date and Publication Date:||
|Date Deposited:||11 Apr 2007|
|Last Modified:||31 Mar 2016 14:08|
Decentralised Data and Information Systems
Funded by: EPSRC (EP/C548051/1)
1 October 2005 to 31 March 2011
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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