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, Weighted Partial MaxSAT, pseudo-Boolean optimisation etc.) over a fixed horizon, and solved optimally using off-the-shelf solvers. One important limitation of this learning and planning framework is the
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issues in Mobile Apps) that have the largest impact on end-users and humanity. Finally, this project will leverage a multi-objective optimisation approach to find a set of optimal QA prioritisation
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intelligence techniques (e.g., Deep Learning, Statistics, ML, Optimization) in order to (1) understand the nature of critical software defects like vulnerabilities; (2) predict; (3) highlight vulnerable code; (4
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on social dilemmas, i.e., situations where poor group outcomes arise from optimal individual choices. We use this framework to study: Multi-agent Systems and AI, Social Systems, and Models in Biology and
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optimal individual choices. We use this framework to study: Multi-agent Systems and AI, Social Systems, and Models in Biology and Evolution. Please check our publications for more details: http
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the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms
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in the learning process to either improve algorithm performance or to complement the information provided by the data. It is a practical guide to optimizing the machine learning process, including
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preferences using real-world spatio-temporal traffic data and open-access consumer surveys. We will develop an large-scale optimisation problem for determining the optimal placement and sizing of charging
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insect colonies. How do independently acting insects achieve a colony-wide optimal or adequate allocation of workforce? How is the required information communicated in the colony? It will also touch on one