AI plays an increasingly significant role in command and control systems (those that support operational command in driving duties and monitoring forces assigned to a mission), with uses ranging from data acquisition and analysis to presenting information to the operator. This article takes a brief look at the present and future contributions of AI in all of those fields.

We will start with data acquisition, where AI shows great potential in two of its main components: adaptive sensor management and sensor data merging. Adaptive management involves an adjustment of measuring parameters and processes and a coordination of sensors to suit each particular mission’s requirements and changes in the environment. The new emerging scenarios (such as hybrid warfare) and new generations of sensors (biometric, social, urban) are promoting new advanced forms of adaptive management. To mention just two examples, techniques like data mining on social media and real-time video analysis will facilitate management based on a more general analysis of the context, going well beyond traditional techniques based on control theory. Meanwhile, the modern Deep Learning algorithms, used in conjunction with classic statistical techniques (like the Bayesian methods, which are on the very edge of what we currently understand as Artificial Intelligence), expand the frontiers of data fusion and are particularly significant for combining unstructured data (like voice data or natural language text) from a wide range of sources. The main challenges are correct alignment or association of the data and management of conflicting data. For instance, to deal with information on the same tactical component coming from different sources, the AI algorithm would take on the task of establishing a relationship between them, resolving the divergences on the basis of a credibility assessment of each source in different situations.

Another command and control area where AI is proving highly useful is decision-making assistance. While at the time of writing, the idea of completely replacing human beings in important decisions still seems a long way off, AI-based tools are already crucial for cutting down the workload of decision makers. Prime examples are situation assessment and choice of the best line of action.

Situation assessment refers to an understanding and interpretation of the operational scenario and possible threats, for the purpose of achieving tactical and strategic goals. The enemy's experience and know-how, as well as own and enemy scholarship, are therefore still essential for properly assessing the situation. AI, nonetheless, makes the task easier by helping the human being in the initial assessment, drawing on the above-mentioned data fusion, data preprocessing, and analysis and providing the operator with the necessary information in a structured and understandable way.

Choosing of the line of action, based in turn on the assessment of the situation, involves an analysis of the possible alternative lines and forecasting the consequences of each one. Once again, the decision maker’s experience and knowledge are the key factors here. However, algorithms like those of machine learning can come in handy in terms of appointing the best decision maker on the basis of the information at hand (i.e., when obtaining additional information would not significantly affect the decision itself); this would be based on a past record of similar experiences. This is crucial. In command and control, after all, timing is everything, and the chances of success are much higher if the decision is made at the right moment. Based on this past record of experiences (consisting basically of a prior knowledge of background facts, decisions, and consequences in comparable situations), the tools are also capable of making a quantitative estimate of the impact associated with each potential alternative, together with the working hypotheses and the corresponding explanation, allowing the human operator to understand why a given decision might cause a given result.

To wind up this brief overview, we will touch on a key element of the presentation of information in command and control systems, the common operational picture (COP). The COP is responsible for showing significant operational information, such as the positions of friendly and enemy units; it is typically associated with a map showing the objects of interest. In classical systems, these objects are fed in manually by a human operator and then displayed to everyone with authorized access to the COP. Machine learning techniques allow information to be added to the map automatically, drawing, for example, on an analysis of satellite images or of a drone sent to reconnoiter the operations area. This way, the objects of interest, such as enemy troops or important infrastructure, can be automatically detected, analyzed and presented to the operator.

As already pointed out above, as things stand, human judgment will continue to be the overriding factor in command and control decision-making for some time yet. Even so, the efficacy now shown by AI-based tools and their sheer speed of development will free humans of some of the more secondary tasks, allowing them to concentrate solely on the areas where human beings still outperform machines.

Author: Raúl Valencia