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This dissertation describes the theoretical foundations for a model of machine-based cognition that is derived from cognitive science. It includes the design, development, and testing of a complete autonomous planning and execution system, which is targeted at the problems of long duration autonomous activity in dynamic and uncertain domains. The formal model is shown to produce more successful plans than classical planning systems, and do so with less computation than Decision Theoretic planning systems. A formal definition of Probability-Aware planning is presented, and critical properties of the algorithm are proven, including decidability, completeness, and correctness. The details of the design and implementation are presented, and the results of comparison with classical planning, and probabilistic planning are presented. When compared with classical planning, three separate problems are used. The Probability-Aware Planning and Execution System is domain independent, and so, no changes to the system are required to solve problems in different domains. One claim of this dissertation is that the Probability-Aware Planning and Execution System will produce plans with higher success rates than classical planners. This is demonstrated, and the results are shown. The second claim is that these improved success rates will be less computationally expensive than using a complete probabilistic analysis. To support this claim a representative probabilisitic planner, based on Markov Decision Problems, is analysed, and the same analysis is applied the Probability-Aware Planning and Execution System. These comparisons show that the computational cost of Probability-Aware planning cannot exceed that of an MDP planner, and is generally far less. These two results show that it is possible for a single planning system to outperform classical planning at lower cost than probabilistic planning. A key contribution of this work is the creation of a new option for the design of autonomous systems that are deployed into dynamic and uncertain domains. The Probability-Aware Planning and Execution System represents a significant improvement in the understanding of the requirements and benefits of heuristic based autonomous systems. It is placed into a framework of complexity ranging from classical planning to fully probabilistic planning. It represents a new model of planning for complex domains.

We present early results from the COMAP Galactic Plane Survey conducted between June 2019 and April 2021, spanning \\(20^\\circ<\\ell<40^\\circ\\) in Galactic longitude and \\(|b|<1.\\!\\!^{\\circ}5\\) in Galactic latitude with an angular resolution of \\(4.5^{\\prime}\\). The full survey will span \\(\\ell \\sim 20^{\\circ}\\)- \\(220^{\\circ}\\) and will be the first large-scale radio continuum survey at \\(30\\) GHz with sub-degree resolution. We present initial results from the first part of the survey, including diffuse emission and spectral energy distributions (SEDs) of HII regions and supernova remnants. Using low and high frequency surveys to constrain free-free and thermal dust emission contributions, we find evidence of excess flux density at \\(30\\,\\)GHz in six regions that we interpret as anomalous microwave emission. Furthermore we model UCHII contributions using data from the \\(5\\,\\)GHz CORNISH catalogue and reject this as the cause of the \\(30\\,\\)GHz excess. Six known supernova remnants (SNR) are detected at \\(30\\,\\)GHz, and we measure spectral indices consistent with the literature or show evidence of steepening. The flux density of the SNR W44 at \\(30\\,\\)GHz is consistent with a power-law extrapolation from lower frequencies with no indication of spectral steepening in contrast with recent results from the Sardinia Radio Telescope. We also extract five hydrogen radio recombination lines to map the warm ionized gas, which can be used to estimate electron temperatures or to constrain continuum free-free emission. The full COMAP Galactic plane survey, to be released in 2023/2024, will be an invaluable resource for Galactic astrophysics.