Maintaining superiority on the battlefield is vital in ensuring mission success. The use of advanced technologies such as Autonomy and Artificial Intelligence offers the ability for the human to let the machines do the heavy lifting of tasks; especially those that require rapid processing of complex information and within a dynamic environment. This paper outlines the MASTER SOUP project that explores and demonstrates the use of AI to manage multiple sensors, whilst also introducing a new method to assist the multidisciplinary design team to better understand how the system deals with decisions that occur during the mission. The use of a method utilizing decision strings is outlined and discussed; whilst using previous studies that have theorized its use, this paper is the first to employ this approach. By adopting this method, it was found that it was beneficial to both Human Factors and AI Engineers in terms of designing the Human-Machine Teaming concept. The use of decision strings provides an intuitive methodology for identifying and deconstructing the nature of decisions within the Human-Machine Team. Further to this it can be used to the benefit of all members of the design team to facilitate the elicitation of design requirements that provide benefits across multidisciplinary fields. It is noted that this approach is new, we hope it will allow other researchers to adopt this methodology and contribute to the validation of this important method used within multidisciplinary design teams.

A new model for evaluating comparison-based algorithms is discussed. Each key has an associated price, and the cost of a comparison is the sum of the prices of the two comparands. Three types of analysis of worst-case performance are presented which differ in the power given to an adversary: the Almighty version, the Just version, and the Merciful version. For example, in the Almighty version (the ‘worst-possible’ case) the adversary knows which algorithm will be used before assigning the prices to the keys and then chooses the worst-case assignments of values to the keys. We give a complete characterisation of optimal algorithms for the problem of finding the maximum key.

We show a one-to-one correspondence between all the ordered trees that have $n_0 + 1$ leaves and $n_i $ internal nodes with $k_i $ sons each, for $i = 1, \cdots ,t$, (hence $n_0 = \sum_1^t (k_i - 1) n_i $) and all the lattice paths in the $(t + 1)$-dimensional space, from the point $(n_0 ,n_1 , \cdots , n_t )$ to the origin, which do not go below the hyperplane $x_0 = \sum_1^t (k_i - 1) x_i $. Procedures for generating these paths (and thus the ordered trees) are presented and the ranking and unranking procedures are derived.