Engineering a wildfire decision support system through the integration of AIXI and the Canadian Fire Weather Index

Fires have been a major source of destruction in Australia, causing enormous ecological and economic damage, as well as loss of human life. Anthropogenic pressure around fire events worldEwide, including Australia, has led to the need for better human decisionEmaking and fire risk evaluation. To this end, fireE\nmodelling and simulation systems have been developed through a variety of forms. However in the existing literature, there is a paucity of information regarding applications of artificial general intelligence in wildfire and natural resource management. The general reinforcement learning method AIXI offers a system capable of making decisions based on an objectively defined reward system and also offers an entirely different approach to natural resource management from conventional decision support systems. \nThis thesis integrates AIXI and the Canadian Fire Weather Index (FWI) System. Both systems share a common structure that makes them amenable to unification in a reinforcement learning framework. This framework forms the basis for their integration into a novel decision support system (FWIEAIXI). \nWith a fully specified FWIEAIXI, the thesis explores the point at which FWIEAIXI can maintain an “acceptable” behaviour in spite of exceptional, unforseen or nonE standard conditions. Through a robustnessEtesting framework, the thesis shows that FWIEAIXI is capable of acting with a diverse range of meteorological inputs. \nThe thesis also provides an information theoretic assessment of FWIEAIXI’s decision making behaviour, where the level of complexity in FWIEAIXI’s decision making process is determined and characterised. For this, six information theoretic measures are introduced. Applications of the six measures show that \nFWIEAIXI utilises prediction, planning and policymaking during its decision making processes. Furthermore, it is shown that FWIEAIXI is a system capable of planning 14 to 21 days into the future in matters of wildfire and natural resource management. \nWith a comparison between FWIEAIXI and human decision making in wildfire management scenarios, this thesis also shows that an iterative application of fuel suppression and the application of controlled burns on only the most favourable of days, is indicative of an optimal wildfire and natural resource management policy.\nThis thesis demonstrates that the application of AIXI in wildfire management scenarios offers a practical demonstration of applied AIXI theory. Finally, the thesis concludes with a discussion of future extension of this work, along with new avenues for research.

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