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Details of Grant 

EPSRC Reference: EP/S001646/1
Title: Bayesian Artificial Intelligence for Decision Making under Uncertainty (BAYES-AI)
Principal Investigator: Constantinou, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Agena Ltd
Department: Sch of Electronic Eng & Computer Science
Organisation: Queen Mary University of London
Scheme: EPSRC Fellowship - NHFP
Starts: 01 June 2018 Ends: 31 May 2021 Value (£): 475,818
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 May 2018 EPSRC UKRI CL Innovation Fellowship Interview Panel 4 - 8 and 9 May 2018 Announced
Summary on Grant Application Form
Scientific research is heavily driven by interest in discovering, assessing, and modelling cause-and-effect relationships as guides for action. Much of the research in discovering relationships between information is based on methods which focus on maximising the predictive accuracy of a target factor of interest from a set of other related factors. However, the best predictors of the target factor are often not its causes and hence, the motto "association does not imply causation". Although the distinction between association and causation is nowadays better understood, what has changed over the past few decades is mostly the way by which the results are stated rather than the way they are generated.

Bayesian Networks (BNs) offer a framework for modelling relationships between information under causal or influential assumptions, which makes them suitable for modelling real-world situations where we seek to simulate the impact of various interventions. BNs are also widely recognised as the most appropriate method to model uncertainty in situations where data are limited but where human domain experts have a good understanding of the underlying causal mechanisms and/or real-world facts. Despite these benefits, a BN model alone is incapable of determining the optimal decision path for a given problem. To achieve this, a BN needs to be extended to a Bayesian Decision Network (BDN), also known as an Influence Diagram (ID). In brief, BDNs are BNs augmented with additional functionality and knowledge-based assumptions to support the representation of decisions and associated utilities that a decision maker would like to minimise or maximise. As a result, BDNs are suitable for modelling real-world situations where we seek to discover the optimal decision path to maximise utilities of interest and minimise undesirable risk.

Because BNs come from statistical and computing sciences, and whereas BDNs come mainly from decision theory introduced in economics, research works between these two fields only occasionally extend from one field to another. As a result, it is fair to say that the landscape of these approaches has matured rather incoherently between these two fields of research. It is possible to develop a new generation of algorithms and methods to improve the way we 'construct' BDNs.

The overall goal of the project is to develop an open-source software that will enable end-users, who may be domain experts and not statisticians, mathematicians, or computer scientists, to quickly and efficiently generate BDNs for optimal real-world decision-making. The proposed system will allow users to incorporate their prior knowledge for information fusion with data, along with relevant decision support requirements for intervention and risk management, but will avoid the levels of manual construction currently required when building BDNs. The system will be evaluated with diverse real-world decision problems including, but not limited to, sports, medicine, forensics, the UK housing market, and the UK financial market.

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Potential use in non-academic contexts
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