Combining cyber and AI in a Battle Management System boosts cybersecurity, situational awareness, decision support, automation, and adaptability. Improving operational efficiency and enabling faster, informed decisions in dynamic military scenarios.

From sci-fi to reality: Unleashing AI’s power in battle management systems

Combining cyber and AI in a Battle Management System boosts cybersecurity, situational awareness, decision support, automation, and adaptability.

In contemporary warfare, the emphasis is placed on disseminating information via a network of bases and electronic combat platforms known as battle management systems (BMS). The advent of automation has given rise to intricate operational environments necessitating collaborative and flexible solutions. The distribution of situational awareness information is vital within modern digital armies, and this is accomplished through dismounted teams, headquarters, and various mobile platforms. This process starkly contrasts the analogue communication methods of the past.

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Like natural systems, the network of bases and electronic combat platforms entails military assets functioning as autonomous entities. Nonetheless, interactions amongst multiple subsystems can precipitate unpredictable intelligence, cybersecurity, and network-enabled technologies results.

What is emergent behaviour?

Emergent behaviour, which involves various tiers within a system, can be encapsulated as a defining attribute of said system. Consequently, military decision-makers must know the ramifications of such emergent behaviour to ascertain secure and predictable system operations. This necessitates a profound understanding of how individual components interact, leading to the emergence of specific patterns. This comprehensive opinion is pivotal to foreseeing and governing future behaviour effectively.

Analysing emergent behaviour assists military decision-makers in forecasting and preparing for potential threats and outcomes. It further enhances strategic decisions through a comprehensive understanding of the system dynamics. Additionally, emergent behaviour can be harnessed to pinpoint areas for improvement and effectuate the requisite alterations. This is particularly vital when reacting to swiftly evolving scenarios. A solid understanding of emergent behaviour can offer invaluable insights and aid in circumventing costly errors.

What is a battle management system (BMS)?

Future advancements will necessitate the expansion of physical capabilities through computation, communication, and control. These entail opportunities and challenges, such as devising next-generation vehicles, facilitating autonomous driving, and creating prosthetics that interface with the brain. Contemporary warfare is steered by the essentiality of disseminating information through a network of bases and electronic combat platforms, collectively referred to as BMS.

BMS is designed to coordinate many assets while delivering situational awareness to commanders. They also foster the integration of novel technologies and capabilities into the battlefield, thus permitting more efficient and effective operations. The proliferation of automation has resulted in intricate operational environments that call for collaborative and adaptable solutions.

Additional opportunities and research hurdles encompass the design and development of next-generation aeroplanes and space vehicles, hybrid gas-electric vehicles, complete autonomous urban driving, and prosthetics that allow brain signals to control physical objects. The mere enhancement of information or data flow efficiency alone modifies the entire organisational construct within which the system functions. Is this where AI proves to be most advantageous?

Combining cyber and AI in a Battle Management System boosts cybersecurity, situational awareness, decision support, automation, and adaptability. Improving operational efficiency and enabling faster, informed decisions in dynamic military scenarios.
Credit. Midjourney

The pivotal role of AI in BMS

Artificial Intelligence (AI) can serve a pivotal role in systems of systems, furnishing intelligent capabilities for managing and controlling interconnected subsystems. Using AI technology for integration, monitoring, decision-making, autonomy, and optimisation can augment the effectiveness, efficiency, and resilience of systems of systems. 

Although systems of systems (SoS) and AI present numerous benefits, there also exist potential drawbacks and challenges associated with their implementation. These adverse impacts and challenges must be acknowledged and addressed for systems and artificial intelligence systems to function effectively and responsibly across various domains. Implementing robust governance frameworks and ethical guidelines and maintaining continuous monitoring is indispensable for risk mitigation and maximisation of the benefits of these technologies.

Cybernetics and AI can go hand in hand

Cybernetics is applied to systems of systems and AI, where Cybernetics is the study of control and communication in complex systems, including systems of systems. When applied to systems of systems and AI, cybernetics provides a framework for understanding and managing the interactions and feedback loops between different components and levels of complexity.

AI plays a crucial role in cybernetics by providing the computational power and intelligence to analyse and control complex systems. AI techniques, such as machine learning, deep learning, and reinforcement learning, can be used to develop models and algorithms for understanding system dynamics, predicting system behaviour, and optimizing control strategies. AI can also automate decision-making processes within systems of systems, enabling real-time responses and adaptive behaviours.

Cybernetics and AI complement each other in the study and application of SoS. Cybernetics provides the theoretical foundations and principles for understanding the dynamics and control of complex systems. AI offers computational tools and techniques to analyse, model, and optimize these systems. Together, they form a robust framework for managing and advancing the capabilities of interconnected systems in various domains, such as transportation, healthcare, energy, and beyond.

This potent amalgamation of cybernetics and AI can be harnessed to devise intelligent systems capable of making autonomous decisions, interacting with humans, and taking proactive actions to enhance the efficiency, safety, and reliability of SoS. Liquid Cybernetic Systems, also called Fourth-Order Cybernetics, can be applied with artificial intelligence (AI) to boost our comprehension and management of complex, adaptive systems.

By integrating Fourth-Order Cybernetics with AI, we can develop more adaptive, reflexive, and contextually aware AI systems that align more closely with the intricacies of real-world systems. This consolidated approach fosters a deeper understanding of complex systems, encourages responsible decision-making, and enables the development of AI systems capable of adapting, co-creating, and co-evolving in dynamic environments.

Integration of cyber and AI technologies into BMS

Integrating cyber and AI technologies into a Battle Management System augments cybersecurity, situational awareness, decision support, command and control automation, and overall adaptability. By exploiting these capabilities, BMS can enhance operational effectiveness, expedite response time, and enable commanders to make more informed decisions in complex and swiftly evolving military scenarios. 

Although cybernetics, systems of systems, and AI present numerous positive advantages, it’s vital to acknowledge potential negative implications. These include possible threats to privacy, data security, and the psychological ramifications of utilising AI-based systems. Ensuring these technologies are implemented responsibly and ethically is of utmost importance. Governments, organisations, and individuals must shoulder responsibility for the ethical use of technology.

Regulations must be established to safeguard against data misuse, privacy violations, and other potential risks AI-based systems pose. Regular monitoring and evaluation should also be undertaken to ensure these technologies are utilised safely and responsibly. Ethical guidelines should also be instituted to ensure corporations are accountable for their technology usage.

A sustainable BMS

Furthermore, promoting education and awareness is crucial to ensure individuals and organisations understand the ethical implications of their technology choices. Users should be enlightened about their rights and the potential risks of utilising these technologies. 

Public policy should be devised to shield the public from potential harm instigated by these technologies. Such policies should embody principles of transparency, accountability, and fairness. Both governments and corporations should be held accountable for their actions and any detrimental effects of their technologies.

Lastly, measures should be instituted to ensure the Australian National University adheres to these policies. Independent regulatory bodies should also be established to oversee AI technologies’ development and ensure their responsible and ethical utilisation. Moreover, public education and awareness programmes should be implemented to ascertain that the public is well-informed about AI technologies’ risks and potential harms.

It is imperative to contemplate and actively address these potential negative impacts through responsible design, rigorous testing, ongoing monitoring, and regulatory frameworks. Striking a balance between capitalising on the benefits of cybernetics, systems of systems, and AI, whilst mitigating possible risks is essential to maximise their positive influence and minimise any adverse consequences.

AI within SOS is available today, yet AI is not anticipated to supplant humans in the near future entirely. Sentient AI suggests that the technology possesses the capability of self-awareness, consciousness, and emotions. However, AI does not possess its autonomous cycle for reproduction.


Journal reference

Seizovic, A., Thorpe, D., Goh, S., & Skoufer, L. (2023). Cybernetics and battle management system (BMS) in network soldier system application. Australian Journal of Multi-Disciplinary Engineering, 1-23.

Aleksandar Seizovic is an IEAust Engineering Executive with interests in engineering, business, and law. In 2016, he was awarded the QLD Engineering Associate of the Year. His research focuses on complex systems such as military "Battle Management Systems (BMS)," which emphasizes the necessity of developing artificial intelligence, complex structural thinking, cybernetics, wicked problem-solving, modelling, and simulation (digital twin), and emergent behavior analysis. This research considers the relationship between complex physical assets and multi-structural systems of systems.