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All Outputs (6)

Privacy preserving record linkage in the presence of missing values (2017)
Journal Article
Chi, Y., Hong, J., Jurek, A., Liu, W., & O'Reilly, D. (2017). Privacy preserving record linkage in the presence of missing values. Information Systems, 71, 199-210. https://doi.org/10.1016/j.is.2017.07.001

© 2017 The problem of record linkage is to identify records from two datasets, which refer to the same entities (e.g. patients). A particular issue of record linkage is the presence of missing values in records, which has not been fully addressed. An... Read More about Privacy preserving record linkage in the presence of missing values.

A novel ensemble learning approach to unsupervised record linkage (2017)
Journal Article
Jurek, A., Hong, J., Chi, Y., & Liu, W. (2017). A novel ensemble learning approach to unsupervised record linkage. Information Systems, 71, 40-54. https://doi.org/10.1016/j.is.2017.06.006

© 2017 Record linkage is a process of identifying records that refer to the same real-world entity. Many existing approaches to record linkage apply supervised machine learning techniques to generate a classification model that classifies a pair of r... Read More about A novel ensemble learning approach to unsupervised record linkage.

The event calculus in probabilistic logic programming with annotated disjunctions (2017)
Presentation / Conference
McAreavey, K., Bauters, K., Liu, W., & Hong, J. (2017, May). The event calculus in probabilistic logic programming with annotated disjunctions. Paper presented at AAMAS 2017 - the 16th International Conference on Autonomous Agents and Multiagent Systems, Sao Paulo, Brazil

We propose a new probabilistic extension to the event calculus using the probabilistic logic programming (PLP) language ProbLog, and a language construct called the annotated disjunction. This is the first extension of the event calculus capable of h... Read More about The event calculus in probabilistic logic programming with annotated disjunctions.

Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments (2017)
Journal Article
Bauters, K., McAreavey, K., Liu, W., Hong, J., Godo, L., & Sierra, C. (2017). Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments. Journal of Artificial Intelligence Research, 58, 731-755. https://doi.org/10.1613/jair.5287

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents – or components – each one modelled based on... Read More about Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments.

Tweet for behavior change: Using social media for the dissemination of public health messages (2017)
Journal Article
Gough, A., Hunter, R. F., Ajao, O., Jurek, A., McKeown, G., Hong, J., …Kee, F. (2017). Tweet for behavior change: Using social media for the dissemination of public health messages. JMIR Public Health and Surveillance, 3(1), https://doi.org/10.2196/publichealth.6313

Background: Social media public health campaigns have the advantage of tailored messaging at low cost and large reach, but little is known about what would determine their feasibility as tools for inducing attitude and behavior change. Objective: Th... Read More about Tweet for behavior change: Using social media for the dissemination of public health messages.

A collaborative multiagent framework based on online risk-aware planning and decision-making (2017)
Journal Article
Palomares, I., Killough, R., Bauters, K., Liu, W., & Hong, J. (2017). A collaborative multiagent framework based on online risk-aware planning and decision-making

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational dec... Read More about A collaborative multiagent framework based on online risk-aware planning and decision-making.