Intelligent Agents Research Articles

Reinforcing Cyber Defense: Generative AI Powered Intelligent Agent Architecture for Enhanced Security Operations

Reinforcing Cyber Defense: Generative AI Powered Intelligent Agent Architecture for Enhanced Security Operations

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Quantum-inspired deep reinforcement learning for adaptive frequency control of low carbon park island microgrid considering renewable energy sources

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning’s experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.

BNPT's Position as The Leading Sector for Eradicating Criminal Acts of Terrorism Based on Law Number 5 Of 2018

<span lang="EN-AU">After the formation of Law Number 5 of 2018 concerning the Eradication of Criminal Acts of Terrorism, it involved collaboration with several other institutions, such as the National Counterterrorism Agency (BNPT) as the leading sector in eradicating criminal acts of terrorism, the National Army (TNI), the Indonesian National Police (POLRI). ) and the State Intelligence Agency (BIN). Aiming at creating a safe and peaceful country, this law is expected to be able to deal with criminal acts of terrorism. However, in reality this law has not been fully implemented optimally in dealing with criminal acts of terrorism in Indonesia. The formulation of the problem in this research is what is the position of BNPT as a leading sector based on Law Number 5 of 2018 concerning the Eradication of Criminal Acts of Terrorism? The method used in this legal research is normative legal research, namely collecting data using secondary data through library research, namely reading from applicable legislation, literature books and documents related to the issues to be discussed. Data processing is based on library data obtained and then analyzed qualitatively. The results of this research are BNPT's position as a leading sector based on Law no. 5 of 2018 has not been optimal in implementing institutional prevention and control efforts to eradicate criminal acts of terrorism. This is because the coordination function between related institutions is currently considered to still be sectoral in handling and eradicating criminal acts of terrorism</span>

Multi-agency counter-terrorism in Britain and Norway: Intelligence agencies and the administration of welfare

This article explores the emergence of new counter-terrorism programmes in the Britain and Norway through which intelligence and security agencies administer welfare. Support and resources are covertly allocated to persons identified as potential threats, through multi-agency structures led by intelligence and security agencies. Unlike Countering Violent Extremism programmes, this multi-agency management is not led by the local municipality; nor are its participants asked to consent to participation. These new programmes are covert. To conceptualize the significance of intelligence agencies entering the space of welfare administration, Mitchell Dean’s work ‘Liberal Government and Authoritarianism’ is used to underline the traditional separation of agencies within liberal governmentality. Governing through freedom entails the ostensible separation of policing, education and care agencies – reflecting the categorization of the population into groups requiring varying levels of coercion to self-govern. To avoid governing too much, each category of population is allocated a specific agency. Traditionally, intelligence and security agencies have been positioned at the highest tier of repression. By entering the space of welfare administration (and thus social policy), intelligence and security agencies have disturbed the sectoral separation integral to liberal governmentality, leaving care and repression in an ever more ambiguous relationship.

‘Profiles in intelligence’: an interview with 8th Mossad chief Danny Yatom

ABSTRACT This article is based on an interview conducted in July 2023 with Danny Yatom, the eighth Mossad chief. He had a distinguished career in the Israeli Defense Force, joining the Mossad in 1996 after his military service. Yatom’s contributions were pivotal during the 1980s and 1990s, particularly in diplomatic engagements and peace negotiations between Israel and Jordan. With experience spanning elite military units, intelligence agencies, Prime Minister’s offices, and the Israeli Parliament, his insights offer a comprehensive understanding of intricate matters, making it the primary reason for my decision to interview Yatom. Among other topics, Yatom provided nuanced perspectives on Israel’s involvement in the Nagorno-Karabakh conflict, its relations with Azerbaijan, the special ties between Israel and Germany, and the 1979 Islamic revolution. I also inquired about the impact of emotions on decision-making in national security, the historical trajectory of the Mossad, and Israel’s approach to Official Public Intelligence Disclosure.

KDPM: Knowledge‐driven dynamic perception model for evacuation scene simulation

AbstractEvacuation scene simulation has become one important approach for public safety decision‐making. Although existing research has considered various factors, including social forces, panic emotions, and so forth, there is a lack of consideration of how complex environmental factors affect human psychology and behavior. The main idea of this paper is to model complex evacuation environmental factors from the perspective of knowledge and explore pedestrians' emergency response mechanisms to this knowledge. Thus, a knowledge‐driven dynamic perception model (KDPM) for evacuation scene simulation is proposed in this paper. This model combines three modules: knowledge dissemination, dynamic scene perception, and stress response. Both scenario knowledge and hazard source knowledge are extracted and expressed. The improved intelligent agent perception model is designed by adopting position determination. Moreover, a general adaptation syndrome (GAS) model is first presented by introducing a modified stress system model. Experimental results show that the proposed model is closer to the reality of real data sets.

Multiagent trajectory prediction with global‐local scene‐enhanced social interaction graph network

AbstractTrajectory prediction is essential for intelligent autonomous systems like autonomous driving, behavior analysis, and service robotics. Deep learning has emerged as the predominant technique due to its superior modeling capability for trajectory data. However, deep learning‐based models face challenges in effectively utilizing scene information and accurately modeling agent interactions, largely due to the complexity and uncertainty of real‐world scenarios. To mitigate these challenges, this study presents a novel multiagent trajectory prediction model, termed the global‐local scene‐enhanced social interaction graph network (GLSESIGN), which incorporates two pivotal strategies: global‐local scene information utilization and a social adaptive attention graph network. The model hierarchically learns scene information relevant to multiple intelligent agents, thereby enhancing the understanding of complex scenes. Additionally, it adaptively captures social interactions, improving adaptability to diverse interaction patterns through sparse graph structures. This model not only improves the understanding of complex scenes but also accurately predicts future trajectories of multiple intelligent agents by flexibly modeling intricate interactions. Experimental validation on public datasets substantiates the efficacy of the proposed model. This research offers a novel model to address the complexity and uncertainty in multiagent trajectory prediction, providing more accurate predictive support in practical application scenarios.

Oil and the US Decision to Overthrow Mosaddeq

On August 19, 1953, Prime Minister Mohammed Mosaddeq was removed from office by a coalition of Iranians, including Mohammed Reza Shah Pahlavi (r. 1941–79) and members of the armed forces, supported by the United States and Great Britain. The US provided considerable financial, logistical, and organizational support to the coup, which was code-named Operation TPAJAX by the Central Intelligence Agency (CIA). Although the British had been committed, in one form or another, to removing Mosaddeq since he first became prime minister and nationalized Iran's British-owned oil industry in May 1951, the US did not determine to overthrow Mosaddeq by coup d'etat until spring 1953, shortly after the administration of President Dwight D. Eisenhower came to office.

تطبيقات الإدارة للذكاء الاصطناعي في اتخاذ القرارات الإدارية دراسة تطبيقية على شركة الاتصالات السعودية

The study aimed to investigate the impact of artificial intelligence, with its dimensions (genetic algorithms, intelligent agents, expert systems), on managerial decision-making, with its aspects (decision-making stages, decision-making determinants, decision-making elements, participation in decision-making) in Saudi Telecommunications Company. To achieve the study objectives, a descriptive-analytical approach was followed. The study population included all employees within Saudi Telecommunications Company. Due to the difficulty of conducting a comprehensive census of all individuals in the study population, the study used a simple random sampling method by distributing the questionnaire link across all social media platforms used by the study population. The study sample comprised 464 individuals who responded to the questionnaire. The study results revealed a statistically significant positive impact of the level of artificial intelligence usage on managerial decision-making in Saudi Telecommunications Company. A strong positive correlation was found between the usage of artificial intelligence and managerial decision-making in the company. The study recommended focusing on providing financial resources, logistical capabilities, and support for the development of artificial intelligence applications that can help maintain the outstanding level of artificial intelligence application usage in Saudi Telecommunications Company. Additionally, efforts should be directed towards providing the technological infrastructure that contributes to enhancing the company's ability to utilize artificial intelligence applications.

Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning

This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.

Dynamic system models and their simulation in the Semantic Web

Modelling and Simulation (M&S) are core tools for designing, analysing and operating today’s industrial systems. They often also represent both a valuable asset and a significant investment. Typically, their use is constrained to a software environment intended to be used by engineers on a single computer. However, the knowledge relevant to a task involving modelling and simulation is in general distributed in nature, even across organizational boundaries, and may be large in volume. Therefore, it is desirable to increase the FAIRness (Findability, Accessibility, Interoperability, and Reuse) of M&S capabilities; to enable their use in loosely coupled systems of systems; and to support their composition and execution by intelligent software agents. In this contribution, the suitability of Semantic Web technologies to achieve these goals is investigated and an open-source proof of concept-implementation based on the Functional Mock-up Interface (FMI) standard is presented. Specifically, models, model instances, and simulation results are exposed through a hypermedia API and an implementation of the Pragmatic Proof Algorithm (PPA) is used to successfully demonstrate the API’s use by a generic software agent. The solution shows an increased degree of FAIRness and fully supports its use in loosely coupled systems. The FAIRness could be further improved by providing more “ rich” (meta)data.

Дани Червения, или как със 142 души се събаря Петата република

The present study draws on a wealth of documentary material on the activities of the French security services such as the Paris Prefecture, intelligence and counter-intelligence, the police and special anti-riot units, as foreign intelligence services such as the CIA, much of which was recently declassified well ahead of the usual term for such sensitive documents. It also includes in-depth interviews, conducted by the author with Bulgarian intelligence officers. This source base makes it possible to analyze in the historical context of the landmark events of May 1968, and the role and activities of Daniel Kohn-Bendit, leader of the March 22 Movement. It becomes possible to present day by day and hour by hour every step of this leader who grows up to be the leader of a nationally representative movement such as the National union of the French students. In addition, his connections with foreign intelligence agencies can be traced. The purpose of the study is to reject the elaborately layered notions and mythologies that Danny the Red was able to “pull the rug” from under de Gaulle’s feet and to destroy his Fifth Republic with only 142 men. Despite his undisputable charisma and qualities as a leader and orator, the cult of which constantly strengthens, this was not possible even for a moment

SPACECRAFT RELATIVE ON-OFF CONTROL VIA REINFORCEMEN T LEARNING

The article investigates the task of spacecraft relative control using reactive actuators, the output of which has two states, “on” or “off”. For cases where the resolution of the thrusters does not provide an accurate approximation of linear control laws using a pulse-width thrust modulator, the possibility of applying reinforcement learning methods for direct finding of control laws that map the state vector and the on-off thruster commands has been investigated. To implement such an approach, a model of controlled relative motion of two satellites in the form of a Markov decision process was obtained. The intelligent agent is presented in the form of “actor” and “critic” neural networks, and the architecture of these modules is defined. It is proposed to use a cost function with variable weights of control actions, which allows for optimizing the number of thruster firings explicitly. To improve the control performance, it is proposed to use an extended input vector for the “actor” and “critic” neural networks of the intelligent agent, which, in addition to the state vector, also includes information about the control action on the previous control step and the control step number. To reduce the training time, the agent was pre-trained on the data obtained using conventional control algorithms. Numerical results demonstrate that the reinforcement learning methodology allows the agent to outperform the results provided by the linear controller with the pulse-width modulator in terms of control accuracy, response time, and number of thruster firings.

Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

Centralized and distributed approaches to control optical point-to-multipoint systems near-real-time

Optical point-to-multipoint (P2MP) connectivity based on digital subcarrier multiplexing (DSCM) has been shown as a solution for the metro-access segment that is able to reduce capital and operational costs and support the capacity and high dynamicity needs of future 6G services. To achieve maximum performance, activation and deactivation of subcarriers must be done near-real-time to provide just the capacity needed to support the input traffic. In this paper, we investigate the applicability of various approaches capable of supporting the near-real-time operation requirement. Starting from the centralized approach that can be carried out on the centralized software-defined networking (SDN) controller, we also explore distributed approaches that might relieve the SDN controller from near-real-time operation. In particular, we explore the performance of deploying a multiagent system (MAS), where intelligent agents run on top of the nodes in the P2MP tree and communicate among them. Illustrative results show that the distributed approaches can achieve a performance close to that of the centralized one, while reducing communication needs. Results also show the importance of traffic/capacity prediction to anticipate the activation of subcarriers.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

Mobile/VR/Robotics/IoT-Based Chatbots and Intelligent Personal Assistants for Social Inclusion

Chatbots are dialogue systems that utilize computational linguistics (CL), including automatic speech recognition (ASR) and natural language processing (NLP). Artificially intelligent conversational agents combine personalization, interoperability, and scalability with the aim of promoting safe information monitoring, management, and retrieval. AI chatbots offer user-friendly engagement for innovative e-learning, care assistance, and multilingual digital content creation, promoting inclusiveness and AI-enhanced communication. They are a breakthrough for future societies and economies as they can offer cost-effective, tailor-made, and instant exchange of knowledge and skills, including on-site assistance, health monitoring, and e-consultation. AI chatbots are constantly improving to the benefit of their users. Their economic and social impact is expected to rise as individuals, especially vulnerable populations, are trained to use them for lifelong learning, decision-making, and problem-solving.

Intelligent agent for hyperspectral image classification with noisy labels: a deep reinforcement learning framework

ABSTRACT Label accuracy plays an important role in supervised hyperspectral image classification problems. However, labelling can be prone to various types of noise, mainly including human errors and incorrect pseudo-label predictions. Learning from datasets with noisy labels is a critical challenge in practice. To address this issue, we first propose a deep reinforcement learning method named HyperRefine to tackle the label noise problem for hyperspectral image classification. The method utilizes an agent to learn the accuracy of each sample label and determine its suitability to be used in training the classifier model. The agent is trained using a reinforcement signal based on the reward obtained from a small validation set that reflects performance on the target task, where the validation set is generated from the training set by a momentum-based recruiter. Compared to complex hand-crafted methods of label noise cleaning, HyperRefine is easy to implement, yet highly effective. The robustness of the model to different types of label noises is significantly improved by further combining a novel label refinement approach. Experiments on artificially added label noise and pseudo-label noise hyperspectral datasets show that the proposed method can achieve promising performance.