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cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational
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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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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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computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be
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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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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