Data driven decision making for complex problems

Grant number: DP170103174 | Funding period: 2017 - 2020

Completed

Abstract

This project aims to formulate methods for using constraint solving and data mining in a complementary and holistic manner. Complex health, educational and social issues require complex decisions supported by automated analysis techniques using rich data sources and human knowledge. Constraint solving and data mining make decisions easier, but are mostly deployed independently, limiting the effectiveness of decisions. This project’s methods should lead to effective and flexible data driven decision making tools for tackling challenging multi-component problems.

Related publications (3)

Improving Single and Multi-View Blockmodelling by Algebraic Simplification

R Ramteke, PJ Stuckey, J Chan, K Ramamohanarao, J Bailey, C Leckie, E Demirovic

2020-07-01

Blockmodelling is an important technique in social network analysis for discovering the latent structures and groupings in graphs...

Computing Optimal Decision Sets with SAT

Jinqiang Yu, Alexey Ignatiev, Peter Stuckey, Pierre Le Bodic

2020-01-01

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably..

Generative image inpainting with submanifold alignment

A Li, J Qi, R Zhang, X Ma, K Ramamohanarao

2019-01-01

Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and ..

University of Melbourne Researchers

James Bailey Computing and Information Systems

Christopher Leckie Computing and Information Systems