Instance-Based Learning Algorithms
Machine Learning
Proving properties of open agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
A survey of data provenance in e-science
ACM SIGMOD Record
Towards content trust of web resources
Web Semantics: Science, Services and Agents on the World Wide Web
The provenance of electronic data
Communications of the ACM - The psychology of security: why do good users make bad decisions?
PrIMe: A methodology for developing provenance-aware applications
ACM Transactions on Software Engineering and Methodology (TOSEM)
iTrust'05 Proceedings of the Third international conference on Trust Management
The Foundations for Provenance on the Web
Foundations and Trends in Web Science
Understanding permissions through graphical norms
DALT'10 Proceedings of the 8th international conference on Declarative agent languages and technologies VIII
Achieving reproducibility by combining provenance with service and workflow versioning
Proceedings of the 6th workshop on Workflows in support of large-scale science
Graphical norms via conceptual graphs
Knowledge-Based Systems
Towards trust in web content using semantic web technologies
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
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Expressing contractual agreements electronically potentially allows agents to automatically perform functions surrounding contract use: establishment, fulfilment, renegotiation etc. For such automation to be used for real business concerns, there needs to be a high level of trust in the agent-based system. While there has been much research on simulating trust between agents, there are areas where such trust is harder to establish. In particular, contract proposals may come from parties that an agent has had no prior interaction with and, in competitive business-to-business environments, little reputation information may be available. In human practice, trust in a proposed contract is determined in part from the content of the proposal itself, and the similarity of the content to that of prior contracts, executed to varying degrees of success. In this paper, we argue that such analysis is also appropriate in automated systems, and to provide it we need systems to record salient details of prior contract use and algorithms for assessing proposals on their content. We use provenance technology to provide the former and detail algorithms for measuring contract success and similarity for the latter, applying them to an aerospace case study.