This thesis presents a new approach to the Arti cial Intelligence (AI) problem of fully automated planning. Planning is the act of deliberation before acting that guides rational behaviour and is a core area of AI. Many practical real-world problems can be classed as planning problems, therefore practical and theoretical developments in AI planning are well motivated. Unfortunately, planning for even toy domains is hard, many different search algorithms have been proposed, and new approaches are actively encouraged. The approach taken in this thesis is to adopt ideas from Evolutionary Algorithms (EAs) and apply the techniques to fully automated plan synthesis. EA methods have enjoyed great success in many problem areas of AI. They are a new kind of search technique that have their foundation in evolution. Previous attempts to apply EAs to plan synthesis have promised encouraging results, but have been ad-hoc and piecemeal. This thesis thoroughly investigates the approach of applying evolutionary search to the fully automated planning problem. This is achieved by developing and modifying a proof of concept planner called GENPLAN. Before EA-based systems can be used, a thorough examination of various parameter settings must be explored. Once this was completed, the performance of GENPLAN was evaluated using a selection of benchmark domains and other competition style planners. The dif culties raised by the benchmark domains and the extent to which they cause problems for the approach are highlighted along with problems associated with EA search. Modi cations are proposed and experimented with in an attempt to alleviate some of the identi ed problems. EAs offer a exible framework for fully automated planning, but demonstrate a clear weakness across a range of currently used benchmark domains for plan synthesis.

Student Number : 0213053E MSc research report - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / One of the risks that have a great impact on society is military con- °ict. Militarised Interstate Dispute (MID) is de¯ned as an outcome of interstate interactions which result in either peace or con°ict. E®ective prediction of the possibility of con°ict between states is a good decision support tool. Neural networks (NNs) have been implemented to predict militarised interstate disputes before Marwala and Lagazio [2004]. Sup- port Vector Machines (SVMs) have proven to be very good prediction techniques in many other real world problems Chen and Odobez [2002]; Pires and Marwala [2004]. In this research we introduce SVMs to predict MID. The results found show that SVM is better in predicting con°ict cases (true positives) without e®ectively reducing the number of correctly classi¯ed peace (true negatives) than NN. A sensitivity analysis for the in°uence of the dyadic (explanatory) variables shows that NN gives more consistent and easy to interpret results than SVM. Further investigation is required with regards to the sensitivity analysis of SVM.

The importance of modelling the spreading of infectious diseases as part of a public health strategy has been highlighted by the ongoing coronavirus pandemic. This includes identifying the geographical areas or travel routes most likely to contribute to the spreading of an outbreak. These areas and routes can then be monitored as part of an early warning system, be part of intervention strategies, e.g. lockdowns, aiming to mitigate the spreading of the disease or be a focus of vaccination campaigns.  This thesis focus on developing a network-based infection model between the municipalities of Sweden in order to identify the areas most likely to contribute to an epidemic. First, a human mobility model is constructed based on the well-known radiation model. Then a network-based SEIR compartmental model is employed to simulate epidemic outbreaks with various parameters. Finally, the adoption of the influence maximization problem known in network science to identify the municipalities having the largest impact on the spreading of infectious diseases.  The resulting super-spreading municipalities point towards confirmation of the known fact that central highly populated regions in highly populated areas carry a greater risk than their neighbours initially. However, once these areas are targeted, the other resulting nodes show a greater variety in geographical location than expected. Furthermore, a correlation can be seen between increased infections time and greater variety, although more empirical data is required to support this claim.   For further evaluation of the model, the mobility network was studied due to its central role in creating data for the model parameters. Commuting data in the Gothenburg region were compared to the estimations, showing an overall good accuracy with major deviations in few cases.