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Weapons systems and platforms guided by Artificial Intelligence can be designed for greater autonomous decision-making with less real-time human control. Their performance will depend upon independent assessments about the relative benefits, burdens, threats, and risks involved with possible action or inaction. An ethical dimension to autonomous Artificial Intelligence (aAI) is therefore inescapable. The actual performance of aAI can be morally evaluated, and the guiding heuristics to aAI decision-making could incorporate adherence to ethical norms. Who shall be rightly held responsible for what happens if and when aAI commits immoral or illegal actions? Faulting aAI after misdeeds occur is not the same as holding it morally responsible, but that does not mean that a measure of moral responsibility cannot be programmed. We propose that aAI include a “Cooperating System” for participating in the communal ethos within NSID/military organizations.

Modelling has been a mainstay of conventional planning support tools (PSTs) since the 1960s and is instrumental in transport and land use planning decision-making. Numerous studies have been conducted to model the potential impacts of emerging vehicle automation and sharing technologies. A systematic review of recent modelling studies of autonomous and shared vehicles in the research literature examines the extent of their contribution to ‘smart’ mobility knowledge. The findings suggest a limited knowledge base from which to support future planning. PSTs that can offer more pluralistic, discursive, and transparent methods in order to understand and proactively shape a transition to a planned urban future are also needed.

The nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.

Using long-run trend data for US tactical and bomber aircraft, Norman Augustine’s Law 16 famously suggests continuously rising US armament unit costs. By the year 2054, the country’s entire defence budget would be expended on a single aircraft, which an industry colleague dubbed as the Battlestar Galactica. However, while it is thought provoking, what does Law 16 in fact entail? It appears that the mechanics of Augustine’s ‘Law’ has never been examined in detail. To help disentangle the matter and assess its relevance in the context of today’s battlefield technology, which is increasingly focussed on the application of large numbers of small, cheap, expendable, electronically linked, yet highly autonomous systems, this chapter introduces the concept of an Augustine weapons system.

Cities evolved into quite complex urban systems. The rigid management process must reflect the complexity of the current political, social, and economic environment. With the vast city growth, citizens experience new difficulties – traffic congestion, pollution, immigration, overcrowding, and inadequate services.

In our research, we analyze problems and benefits that occur with the growing complexity and offer a new concept considering every city as a live and constantly developing complex adaptive system of many participants and actors that operate in an uncertain environment. These actors (residents, businesses, transport, energy, water supply providers, entertainment, and others) are the main elements of city life.

The new concept of “Smart City 5.0” is based on a previously developed model of Smart City 4.0 (compared with Industry 4.0) and implements the Urban Digital Ecosystem, where every element can be represented by a smart agent operating on its behalf. It is shown that smart services can interact vertically and horizontally in the proposed ecosystem, supporting competition and cooperation behavior based on specialized network protocols for balancing the conflicting interests of different city actors.

The chapter describes the design principles and the general architecture of the Urban Digital Ecosystem, including the basic agent of smart service, protocols of the agent’s negotiation, the architecture, and basic principles Smart City knowledge base.

The developed evolutionary methodology of implementation will ensure a minimum of disruptions to city services during its transformation into an urban ecosystem to harmoniously balance all spheres of life and the contradictory interests of different city actors.

Based on a synthesis of findings from psychology, neuroscience, and machine learning, we propose a unified theory of curiosity as a form of motivated cognition. Curiosity, we propose, is comprised of a family of mechanisms that range in complexity from simple heuristics based on novelty, salience, or surprise, to drives based on reward and uncertainty reduction and finally, to self-directed metacognitive processes. These mechanisms, we propose, have evolved to allow agents to discover useful regularities in the world – steering them toward niches of maximal learning progress and away from both random and highly familiar tasks. We emphasize that curiosity arises organically in conjunction with cognition and motivation, being generated by cognitive processes and in turn, motivating them. We hope that this view will spur the systematic study of curiosity as an integral aspect of cognition and decision making during development and adulthood.