After-Action Review for AI (AAR/AI)

Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to assess an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways—leading to the possibility of wide variations in assessment of the same agent due only to variations in their processes. AAR, or After-Action Review, is a method some military organizations use to assess human agents, and it has been validated in many domains. Drawing upon this strategy, we derived an After-Action Review for AI (AAR/AI), to organize ways people assess reinforcement learning agents in a sequential decision-making environment. We then investigated what AAR/AI brought to human assessors in two qualitative studies. The first investigated AAR/AI to gather formative information, and the second built upon the results, and also varied the type of explanation (model-free vs. model-based) used in the AAR/AI process. Among the results were the following: (1) participants reporting that AAR/AI helped to organize their thoughts and think logically about the agent, (2) AAR/AI encouraged participants to reason about the agent from a wide range of perspectives , and (3) participants were able to leverage AAR/AI with the model-based explanations to falsify the agent’s predictions.

The Challenges of Simulation Training of the Troops in the Context of the Emerging Technologies

Training using different simulation systems has become a necessity for the military organizations over the past decades. As the operational environment is constantly changing and the technologies are evolving, simulation systems have also developed. Depending on the domain and branch the simulation training is used for, there have been serious challenges in adapting the systems to the current technological era. Moreover, the emerging technology had a great impact on the military training using simulation systems and has brought an advantage to this domain. Consequently, individual training, collective training, special skills training using simulation systems have been some of the aspects that have been consistently changed and improved over the past years. Technologies such as virtual reality (VR), augmented reality (AR), artificial intelligence (AI), soldier simulators and the initiative of converging constructive and virtual simulation through cloud technologies are some of the major sciences that have been starting to be developed, tested and used for military training purposes. Nevertheless, technological development and operational changes need to also face the challenges of interoperability of the simulation systems that would eventually need to be connected in the inevitable context of integrated, distributed, joint or multinational events.

Machine Learning-based Mist Computing Enabled Internet of Battlefield Things

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices, wearables, sensors, and micro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions.

Climate Security and Policy Options in Japan

Climate security has been discussed in both academia and policy documents in the West. A key point that surfaces from these discussions is that the cooperation of non-military organizations is essential for effective responses to climate change-related threats. This overlaps considerably with debates on security in Japan, where the use of force is constitutionally restricted. Therefore, it is possible to localize the concept of climate security to the genealogy of Japan’s security policy that, in the 1980s and 1990s, sought a non-traditional security strategy that did not rely solely on military power in the name of “comprehensive security,” “environmental security,” and “human security.” In Japan, the perspective of climate security is rare. However, the introduction of a unique climate security concept into security policy enables the maintenance of national security and environmental conservation. Additionally, struggling with climate change alongside neighboring countries contributes to mutual confidence building and stability in international relations in Northeast Asia. To achieve this objective, we first show that climate security includes many kinds of security concerns by surveying previous studies and comparing Western countries’ climate security policies. Second, we follow the evolution of Japan’s security policy from 1980 to 2021. Finally, we review Japanese climate security policies and propose policy options.

MOTIVATIONAL FEATURES OF YOUNG OFFICERS

This article highlights the topic of the motivational sphere of young servicemen. Motivation is a complex psychological phenomenon that provides opportunities for the development of a personality, its training and ensures its mental health. Motivation diagnostics is an important component of psychological service in military organizations. The manifestation of negative motivation can be an important indicator of maladaptation. Diagnostics of motivation among young officers showed that they have higher values of positive motivation compared to civilians. The indicators of male and female officers were compared. The analysis did not identify statistically significant differences between the indicators of male and female officers.

TRENDS IN THE TRANSFORMATION OF DEPARTMENTAL LOGISTICS SYSTEMS IN THE CONTEXT OF A NEW TECHNOLOGICAL ORDER AND RISING GEOPOLITICAL TENSIONS

The paper describes changes in models of functioning of economic agents in the digital economy. It is demonstrated that an important trend of the global economy is a partial rejection of globalization and increase of importance of local production. In this situation departmental logistics systems should replace purchases by implementation of efficient models of access of military organizations to resources, build up long term cooperation with commercial suppliers and contribute to development of national production of resources.