Intelligent Agents Research Articles

Negotiation-based scheduling considering agent emotion

Scheduling in decentralized manufacturing systems is being expanded and transformed to a smart level where manufacturing tasks are executed autonomously. However, achieving self-organized manufacturing resources that can make collaborative decisions remains challenging. This paper presents a novel negotiation model for decentralized scheduling in an autonomous manufacturing system. A variety of emotions represent the willingness of autonomous intelligent agents (IAs) to negotiate for a consensus despite their diverse goals. The proposed communication protocol considers the emotional aspects of the autonomous machine agents in decentralized decision-making. The negotiation model focuses on analyzing the effects of two key emotions: selfishness and cooperation. This study constitutes the basis for experiments on multi-goal machine tasks and artificial emotion mechanism in a flow shop production system. The complexities of negotiation dynamics between IAs, where the interplay of selfish and cooperative strategies influences the outcomes and efficiency of reaching agreements, are explored. The experimental results show that selfish IAs eventually reach agreements through gradual compromise, but cooperative IAs, with their flexibility, can quickly achieve resolutions, especially when bargaining power is balanced. Aligning with the dual concern theory, this study reveals that while selfish IAs can dominate cooperative ones, the cooperative approach often leads to faster and more effective outcomes, regardless of the level of selfishness involved.

The politicization of CIA intelligence: Four cases compared

The political activism of current and former employees of the Central Intelligence Agency before and during the presidency of Donald Trump has been widely observed but not compared to previous episodes of politicization of intelligence by CIA personnel. Organizational cultural norms that long proscribed political activism by CIA employees eventually encouraged it. Societal trends account for some of the evolution, but much reflects purposeful engineering by President Barack Obama and his appointees. These changes, amplified by President Joe Biden, are likely to lead to renewed activism in the future—damaging national security and public confidence in the CIA.

Book Review – Intelligence Overview: The Watchers and the Watchdog

Book Review by Dr Peter Grace Didier Bigo, Emma Mc Cluskey, Felix Tréguer (eds), (2024), Intelligence Oversight in Times of Transnational Impunity. Who will Watch the Watchers? Oxon: Routledge. ISBN 9781032406541. 310 pages. This book focuses on two important issues in intelligence oversight: the long-standing acceptance of raison d’état, and the increasing relations between intelligence agencies transnationally and how much those should come under scrutiny and legal restraint. It is not a book that is designed to be read comfortably by intelligence practitioners, and indeed there are priors that suggest practitioners would much prefer it if there was no oversight of their activities at all. Whether framing the enquiry in this way provokes some degree of urgency or simply divides opinion, the questions of raison d’état and transnational oversight are important.

Medical Intelligence And Public Health In The Complexities Of Contemporary Societies The Role Of FBI In The United States

The term Medical Intelligence (Medint) defines the activities of collection, evaluation, analysis and interpretation of medical, bio-scientific and environmental information, regarding a specific country or geographical area, carried out mainly by military or intelligence agencies and carried out abroad, and useful for military or humanitarian strategic planning. It is of interest to Medint to know the potential of local health facilities, both civil and military, in the diagnostic evaluation, in case of infectious pathologies and in the evaluation of environmental risks as well as the "state of the art" of the development of computerized diagnostic technology and of biotechnology. The SMI (Strategic Medical Intelligence) is instead institutionally delegated to the intelligence agencies that deal with territorial control (counter-terrorism). In the United States, the SMI bioterrorism experts depend on the FBI. According to the definition offered by the FBI, Strategic Medical Intelligence (SMI) deals with all the management activities of a medical or health crisis situation following a terrorist act.

Musician-AI partnership mediated by emotionally-aware smart musical instruments

The integration of emotion recognition capabilities within musical instruments can spur the emergence of novel art formats and services for musicians. This paper proposes the concept of emotionally-aware smart musical instruments, a class of musical devices embedding an artificial intelligence agent able to recognize the emotion contained in the musical signal. This spurs the emergence of novel services for musicians. Two prototypes of emotionally-aware smart piano and smart electric guitar were created, which embedded a recognition method for happiness, sadness, relaxation, aggressiveness and combination thereof. A user study, conducted with eleven pianists and eleven electric guitarists, revealed the strengths and limitations of the developed technology. On average musicians appreciated the proposed concept, who found its value in various musical activities. Most of participants tended to justify the system with respect to erroneous or partially erroneous classifications of the emotions they expressed, reporting to understand the reasons why a given output was produced. Some participants even seemed to trust more the system than their own judgments. Conversely, other participants requested to improve the accuracy, reliability and explainability of the system in order to achieve a higher degree of partnership with it. Our results suggest that, while desirable, perfect prediction of the intended emotion is not an absolute requirement for music emotion recognition to be useful in the construction of smart musical instruments.

Use Of Medical Tools In Gaining Vital Intelligence For National Security Purposes

The definition of Medical Intelligence is by all accounts a broad and complex scientific task that needs to encompass wider ramifications of using medical and biological data to advance national security interests. The paper focuses on examples of using the medical discipline in the form of an exhaustive collection of medical data in order to arrive at very specific results significant in the pursuit of national security or political stability, albeit only from the point of view of a particular actor – the state. The three representative cases are illustrated to arrive at a fundamental understanding of how the medical data – intelligence – can be used if the medical environment is burdened by the imperative to further strategic state-devised objectives. In the organization of a fictitious inoculation campaign in rural Pakistan by the Central Intelligence Agency in 2011 to track down the whereabouts of Osama bin Ladin through mapping the DNA pattern of purported bin Ladin’s relatives, the state actor focused specifically on collection of human biological samples while deploying covert tactics – both disregarding the purported motive of the “health campaign” as well as executing the operation on the foreign soil. In another example, perceiving the potential uncontrolled population growth as a direct threat to both vital national interests, as well as the country’s public image, Uzbekistan, on the state level, has engaged in mass forced sterilization programs to curb the national birth rate. In a similar perception, the treatment of the rare disease of leprosy in Uzbekistan historically encompassed methods incompatible with medical ethics and basic human rights.The precedents will be examined as part of the wider discourse on what constitutes Medical Intelligence, and what is permissible within the framework of national security concerns.

Open-Vocabulary Predictive World Models from Sensor Observations.

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots.

Open-Vocabulary Part-Level Detection and Segmentation for Human–Robot Interaction

Object detection and segmentation have made great progress in robotic application missions. However, intelligent agents require fine-grained recognition algorithms rather than object-level and language instructions to enhance the interaction between humans and robots. To improve the robot’s interactivity in the process of the robot response to language instructions, we propose a method for part-level detection and segmentation by exploiting vision language models. In this approach, Swin Transformer is introduced in the image encoder for extracting image features, and FPNs (Feature Pyramid Networks) are modified to better process the features from Swin Transformer. Next, the image decoder is proposed for model aligning between the image features and text embeddings for achieving human–robot interaction via language. Finally, we verify that the text embeddings are impacted by the command of input and that different prompt templates also affect classification. The method proposed in this paper, which is validated on two datasets (PartImagePart and Pascal Part), possesses the ability to understand and execute part-level missions and accurately segments and detects parts compared with existing interactive methods.

Vicente Lombardo Toledano and the Soviet Approach to the Mexican Left After the Second World War

In the history of relations between the USSR and Latin American states, support for the Soviet foreign policy course from non-communist forces – national reformist parties, trade unions, and leftist intelligentsia associations – was of great importance. The Mexican syndicalist Vicente Lombardo Toledano, founder of the Confederation of Latin American Workers and the Socialist People’s Party, had the greatest political weight among the members of all these organizations. He proclaimed himself a supporter of orthodox Marxism, always supported the USSR in the international arena, and actively interacted with the communists. In this article, the author examines the formation of Lombardo Toledano’s views, his ideas about the significance of the Russian revolution and the USSR for the development of Mexico and other Latin American countries. He notes that communication with him helped the Soviet party and state leadership to avoid excessive dogmatism in assessing the domestic and foreign policies of Mexico in the 1940s–1960s. The author places special emphasis on the extent to which contacts with Lombardo Toledano enabled the CPSU Central Committee and the Soviet Ministry of Foreign Affairs to better understand the state of the Mexican left-wing forces and, above all, the Communist Party of Mexico. The author also demonstrates that he was of interest to Soviet strategists as someone who both had access to the Mexican ruling elite and enjoyed prestige in the Latin American and international labour and anti-war movements. He shows that Lombardo Toledano tried to benefit politically and materially from friendly relations with the USSR, while in return supporting Moscow in its struggle against its ideological opponents such as the Trotskyists and Maoists. The source base of the study comprises Lombardo Toledano’s polemical and propaganda writings, documents from Russian archives (the Russian State Archive of Social and Political History and the Russian State Archive of Contemporary History), declassified archival materials and published documents emanating from the U.S. Department of State and the Central Intelligence Agency.

Joining SUMO and Unreal Engine to Create a Bespoke 360 Degree Narrow Passage Driving Simulator

The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.

Intelligent Agents at School—Child–Robot Interactions as an Educational Path

The pervasiveness of technologies leads us to talk about a code society. From an educational point of view, coding, computational thinking, and educational robotics are an open possibility. Nevertheless, new elements such as artificial intelligence are rapidly changing educational technology perspectives. In this work, we will analyze school policies and theoretical bases in order to understand if, and under what kind of, condition coding, computational thinking, and educational robotics still represent the qualifying elements of a framework for digital literacy and digital citizenship.

The Legal System of Transactions and the Challenges of the Metaverse World in accordance with UAE Law

The world has undergone important technological transformations, making commercial transactions move from normal to electronic and linked to the real world. Still, the new generation of the Internet led to the emergence of the Metaverse world, which attracted many companies and governments, including the UAE, to pay attention to and invest in it. It is based on the use of Blockchain and Artificial Intelligence. This has affected the commercial activities of companies, as it has made contracts done smartly. Its subject will be non-fungible tokens, which is the subject of the study that emphasized the leadership of the UAE government and companies in its interest in the technologies of the Fourth Industrial Revolution and the world of Metaverse and its legislative exclusivity in regulating virtual assets and encrypted tokens and the intelligent agent, but that remains insufficient to control transactions in the Metaverse world.

Optimal scheduling of smart home energy systems: A user-friendly and adaptive home intelligent agent with self-learning capability

This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO2 emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.

Artificial Intelligent Agent Architecture and Clinical Decision-Making in the Healthcare Sector.

This paper examines the decision-making processes of physicians and intelligent agents within the healthcare sector, particularly focusing on their characteristics, architectures, and approaches. We provide a theoretical insight into the evolving role of artificial intelligence (AI) in healthcare, emphasizing its potential to address various healthcare challenges. Defining features of intelligent agents are explored, including their perceptual abilities and behavioral properties, alongside their architectural frameworks, ranging from reflex-based to general learning agents, and contrasted with the rational decision-making structure employed by physicians. Through data collection, hypothesis generation, testing, and reflection, physicians exhibit a nuanced approach informed by adaptability and contextual understanding. A comparative analysis between intelligent agents and physicians reveals both similarities and disparities, particularly in adaptability and contextual comprehension. While intelligent agents offer promise in enhancing clinical decisions, challenges with types of dataset biases pose significant hurdles. Informing and educating physicians about AI concepts can build trust and transparency in intelligent programs. Such efforts aim to leverage the strengths of both human and AI toward improving healthcare delivery and outcomes.

From large language models to small logic programs: building global explanations from disagreeing local post-hoc explainers

The expressive power and effectiveness of large language models (LLMs) is going to increasingly push intelligent agents towards sub-symbolic models for natural language processing (NLP) tasks in human–agent interaction. However, LLMs are characterised by a performance vs. transparency trade-off that hinders their applicability to such sensitive scenarios. This is the main reason behind many approaches focusing on local post-hoc explanations, recently proposed by the XAI community in the NLP realm. However, to the best of our knowledge, a thorough comparison among available explainability techniques is currently missing, as well as approaches for constructing global post-hoc explanations leveraging the local information. This is why we propose a novel framework for comparing state-of-the-art local post-hoc explanation mechanisms and for extracting logic programs surrogating LLMs. Our experiments—over a wide variety of text classification tasks—show how most local post-hoc explainers are loosely correlated, highlighting substantial discrepancies in their results. By relying on the proposed novel framework, we also show how it is possible to extract faithful and efficient global explanations for the original LLM over multiple tasks, enabling explainable and resource-friendly AI techniques.

Graph recurrent neural networks-integrated real-time prediction of key displacements for fire-induced collapse early warning of steel frames

This study proposes the FRAME-Net, which can extract spatial-temporal features of discrete structures like planar steel frames, for early warning systems of fire-induced building collapse. FRAME-Net, based on graph neural networks and recurrent neural networks, seamlessly integrates feature extraction techniques, advanced graph convolution operations, and an enhanced graph long short-term memory mechanism, resolving the critical issue that the actual state of burning buildings, e.g., fire scenario, load case, material properties, etc., cannot be identified accurately and rapidly. The temperatures of steel components and easy-to-measure displacements are inputs, and hard-to-measure displacements at the top and interior of steel frames under fire can be predicted in real time. Numerical examples indicate that the trained agent accurately predicts the hard-to-measure displacements for the trained steel frame under completely unknown datasets, showcasing its potential in real-world unknown fire scenarios. Besides, the trained intelligent agent can be directly applied to untrained steel frames with different topological forms without re-training, which significantly reduces computational costs and is beneficial for swift emergency responses. Subsequently, satisfactory results predicted by the trained agent without re-training for the steel frame under the real fire test further underscores the promising generalization capability of the trained agent to adapt to unknown fire scenarios in real-world steel frames. Finally, the feasibility of the proposed framework to facilitate the implementation of the early-warning systems of fire-induced collapse is proved by comparing the predicted and actual remaining building collapse time, thereby assisting in reducing casualties during the rescue process and contributing to smart firefighting advancements.

Quantum-inspired modeling of distributed intelligence systems with artificial intelligent agents self-organization

Distributed intelligence systems (DIS) containing natural and artificial intelligence agents (NIA and AIA) for decision making (DM) belong to promising interdisciplinary studies aimed at digitalization of routine processes in industry, economy, management, and everyday life. In this work, we suggest a novel quantum-inspired approach to investigate the crucial features of DIS consisting of NIAs (users) and AIAs (digital assistants, or avatars). We suppose that N users and their avatars are located in N nodes of a complex avatar - avatar network. The avatars can receive information from and transmit it to each other within this network, while the users obtain information from the outside. The users are associated with their digital assistants and cannot communicate with each other directly. Depending on the meaningfulness/uselessness of the information presented by avatars, users show their attitude making emotional binary “like”/“dislike” responses. To characterize NIA cognitive abilities in a simple DM process, we propose a mapping procedure for the Russell’s valence-arousal circumplex model onto an effective quantum-like two-level system. The DIS aims to maximize the average satisfaction of users via AIAs long-term adaptation to their users. In this regard, we examine the opinion formation and social impact as a result of the collective emotional state evolution occurring in the DIS. We show that generalized cooperativity parameters Gi\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G_i$$\\end{document}, i=1,⋯,N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i=1,\\dots,N$$\\end{document} introduced in this work play a significant role in DIS features reflecting the users activity in possible cooperation and responses to their avatar suggestions. These parameters reveal how frequently AIAs and NIAs communicate with each other accounting the cognitive abilities of NIAs and information losses within the network. We demonstrate that conditions for opinion formation and social impact in the DIS are relevant to the second-order non-equilibrium phase transition. The transition establishes a non-vanishing average information field inherent to information diffusion and long-term avatar adaptation to their users. It occurs above the phase transition threshold, i.e. at Gi>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G_i>1$$\\end{document}, which implies small (residual) social polarization of the NIAs community. Below the threshold, at weak AIA–NIA coupling (Gi≤1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G_i\\le 1$$\\end{document}), many uncertainties in the DIS inhibit opinion formation and social impact for the DM agents due to the information diffusion suppression; the AIAs self-organization within the avatar–avatar network is elucidated in this limit. To increase the users’ impact, we suggest an adaptive approach by establishing a network-dependent coupling rate with their digital assistants. In this case, the mechanism of AIA control helps resolve the DM process in the presence of some uncertainties resulting from the variety of users’ preferences. Our findings open new perspectives in different areas where AIAs become effective teammates for humans to solve common routine problems in network organizations.

Advertising Artificial Intelligence (AI) Agents: The Effects of Social Presence, Sincerity, and Social Benefit Appeals

The present study investigates the distinct advantages that conversational artificial intelligence (AI) agents provide consumers, including social benefits and communication sincerity. Additionally, this research examines how the exhibition of these attributes in advertisements impacts consumer perceptions. In doing so, this study represents an initial look at how advertisers can strategically promote conversational AI agents to consumers. The present study employed a 2 (AI agent sincerity condition vs. control condition) × 2 (AI agent benefits appeal: social benefits condition vs. utilitarian benefits condition) between-subjects online experimental design with a sample of 256 U.S. adults. Study results suggest that promoting social benefits of AI agents and emphasizing a sincere personality influence consumer social presence perceptions in human–AI interaction. Consumers’ perceived social presence leads to more favorable attitudes toward the advertisement and the use of the AI agent. In contrast, without the mediating role of social presence, accentuating the utilitarian benefits of AI agents in the advertisement is more effective than highlighting social advantages in influencing consumers’ positive attitudes. The current study contributes to the literature on consumer AI interactions, social presence, parasocial interactions, and brand personalities.

Intelligent Recognition of Tool Wear with Artificial Intelligence Agent

Tool wear, closely linked to operational efficiency and economic viability, must be detected and managed promptly to prevent significant losses. Traditional methods for tool wear detection, though somewhat effective, often lack precision and require extensive manual effort. Advancements in artificial intelligence (AI), especially through deep learning, have significantly progressed, providing enhanced performance when combined with tool wear management systems. Recent developments have seen a notable increase in the use of AI agents that utilise large language models (LLMs) for specific tasks, indicating a shift towards their integration into manufacturing processes. This paper provides a comprehensive review of the latest advancements in AI-driven tool wear recognition and explores the integration of AI agents in manufacturing. It highlights the LLMS and the various types of AI agents that enhance AI’s autonomous capabilities, discusses the potential benefits, and examines the challenges of this integrative approach. Finally, it outlines future research directions in this rapidly evolving field.

Temporally extended successor feature neural episodic control

One of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals. In this paper, we introduce an episodic control framework based on the temporal expansion of subsequent features to achieve these goals, which we refer to as Temporally Extended Successor Feature Neural Episodic Control (TESFNEC). This method has shown impressive results in significantly improving sample efficiency and elegantly reusing previously learned strategies. Crucially, this model enhances agent training by incorporating episodic memory, significantly reducing the number of iterations required to learn the optimal policy. Furthermore, we adopt the temporal expansion of successor features a technique to capture the expected state transition dynamics of actions. This form of temporal abstraction does not entail learning a top-down hierarchy of task structures but focuses on the bottom-up combination of actions and action repetitions. Thus, our approach directly considers the temporal scope of sequences of temporally extended actions without requiring predefined or domain-specific options. Experimental results in the two-dimensional object collection environment demonstrate that the method proposed in this paper optimizes learning policies faster than baseline reinforcement learning approaches, leading to higher average returns.