Agent Communication Research Articles

DYNAMICS OF THE STRUCTURE OF SOCIAL REPRESENTATIONS OF ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF SOCIAL CHANGE

The aim of the research is to study the dynamics of the structure of social representations of artificial intelligence (AI) among non-professionals. 258 students of different faculties aged from 18 to 24 took part in this study. The study took place in two stages: 1) November 2021, 2) March 2022. To identify the structure of the social perception of AI, the Vergès method was used based on the material of free associations. At the second stage, the level of respondents’ concern about the events of recent weeks in the economic and political spheres was additionally monitored. It was found that elements related to expectations of AI capabilities have strengthened the central core of the representation. Words with a connotation of danger appear in the periphery elements. Probably it is caused by both the activities of communication agents and the context of disturbing social changes. Against this background, it is important to enrich the knowledge of AI among ordinary people in order to form an adequate level of trust and distrust of recommendations based on it.

Misinformation and higher-order evidence

This paper uses computational methods to simultaneously investigate the epistemological effects of misinformation on communities of rational agents, while also contributing to the philosophical debate on ‘higher-order’ evidence (i.e. evidence that bears on the quality and/or import of one’s evidence). Modelling communities as networks of individuals, each with a degree of belief in a given target proposition, it simulates the introduction of unreliable mis- and disinformants, and records the epistemological consequences for these communities. First, using small, artificial networks, it compares the effects, when agents who are aware of the prevalence of mis- or disinformation in their communities, either deny the import of this higher-order evidence, or attempt to accommodate it by distrusting the information in their environment. Second, deploying simulations on a large(r) real-world network, it observes the impact of increasing levels of misinformation on trusting agents, as well as of more minimal, but structurally targeted, unreliability. Comparing the two information processing strategies in an artificial context, it finds that there is a (familiar) trade-off between accuracy (in arriving at a correct consensus) and efficiency (in doing so in a timely manner). And in a more realistic setting, community confidence in the truth is seen to be depressed in the presence of even minimal levels of misinformation.

Role of Exosomes in Salivary Gland Tumors and Technological Advances in Their Assessment.

Backgroud: Salivary gland tumors (SGTs) are rare and diverse neoplasms, presenting significant challenges in diagnosis and management due to their rarity and complexity. Exosomes, lipid bilayer vesicles secreted by almost all cell types and present in all body fluids, have emerged as crucial intercellular communication agents. They play multifaceted roles in tumor biology, including modulating the tumor microenvironment, promoting metastasis, and influencing immune responses. Results: This review focuses on the role of exosomes in SGT, hypothesizing that novel diagnostic and therapeutic approaches can be developed by exploring the mechanisms through which exosomes influence tumor occurrence and progression. By understanding these mechanisms, we can leverage exosomes as diagnostic and prognostic biomarkers, and target them for therapeutic interventions. The exploration of exosome-mediated pathways contributing to tumor progression and metastasis could lead to more effective treatments, transforming the management of SGT and improving patient outcomes. Ongoing research aims to elucidate the specific cargo and signaling pathways involved in exosome-mediated tumorigenesis and to develop standardized techniques for exosome-based liquid biopsies in clinical settings. Conclusions: Exosome-based liquid biopsies have shown promise as non-invasive, real-time systemic profiling tools for tumor diagnostics and prognosis, offering significant potential for enhancing patient care through precision and personalized medicine. Methods like fluorescence, electrochemical, colorimetric, and surface plasmon resonance (SPR) biosensors, combined with artificial intelligence, improve exosome analysis, providing rapid, precise, and clinically valid cancer diagnostics for difficult-to-diagnose cancers.

VAEnvGen: A Real-Time Virtual Agent Environment Generation System Based on Large Language Models

Environment plays an important role in non-verbal communication for human-virtual agent interaction. Existing research explores the influence of an agent’s appearance and attributes to enhance human-virtual agent communication. However, there is no common practice for dynamically adjusting the surrounding environments of the virtual agent. In this paper, we introduce a real-time virtual agent environment generation system (VAEnvGen), which contributes to the field by enhancing users’ content perception and improving task performance through dynamic environment adjustment. The system dynamically analyzes both the appropriate communication environment and filters the key information according to the current context. Leveraging Large Language Models, it generates a pseudo-3D background space to create an engaging atmosphere and a dynamic foreground content space for vivid key information display, thereby significantly enhancing content perception. For widespread adoption and flexibility, VAEnvGen is developed as a web application. We further evaluate the impact of VAEnvGen on content perception, user attention, and subjective satisfaction through a mixed-design user study with 50 participants. Quantitative and qualitative results reveal significant improvements in content perception, task completion time, and user satisfaction when using VAEnvGen. The system effectively redistributes user attention from subtitles and the virtual agent itself to the dynamically generated background and key foreground information, leading to a more immersive and less fatiguing user experience.

Blame as participant anger: extending moral claimant competence to young children and nonhuman animals

ABSTRACT Following the social conception of moral agency, this paper claims that many agents commonly exempted from moral responsibility, like young children, adults with late-stage dementia, and nonhuman animals, may nevertheless qualify as participants in moral responsibility practices. Blame and other moral responsibility responses are understood according to the communicative emotion account of the reactive attitudes. To blame someone means having an emotion episode that acts as a vehicle for conveying a particular moral content. Therefore, moral agency is argued to be manifested in communicative exchanges between a claimant and a defendant. While many human and nonhuman agents are justifiably exempted from ascriptions of moral responsibility, this does not necessarily exclude such agents from the community of moral agents altogether. Toddlers and dogs, for instance, seem capable of other-directed reactive attitudes, like resentment, and could, therefore, qualify as participants in moral responsibility practices with respect to the claimant position. Therefore, we may have reason to adopt a distinct claimant-directed participant stance to some beings, even if they fail to qualify as apt targets of blame. This expanded theoretical room for moral agency is argued to make explicit further normative considerations.

Reinforced steering Evolutionary Markov Chain for high-dimensional feature selection

The increasing accessibility of extensive datasets has amplified the importance of extracting insights from high-dimensional data. However, the task of selecting relevant features in these high-dimensional spaces is made more difficult due to the curse of dimensionality. Although Evolutionary Algorithms (EAs) have shown promise in the literature for feature selection, creating EAs for high dimensions is still challenging. To address the problem of feature selection in high dimensions, a novel concept of Evolutionary Reinforced Markov Chain is proposed in this paper. The proposed work has the following contributions and merits: (i) The paradigms of evolutionary computation, reinforcement learning, and Markov chain are incorporated into an integrational framework for feature selection in high dimensional spaces in a recursive manner. (ii) To support the global convergence of the algorithm and manage its computational complexity, a restricted group of the most effective agents is maintained within the evolutionary population. (iii) The dynamic Markov chain process efficiently manages agent evolution and communication, ensuring effective navigation through the search space. (iv) Agents moving in the right way are rewarded with an increase in their associated transition probability, while the agents going in the wrong direction are discouraged with a decrease in their associated transition probabilities; this promotes the establishment of an equilibrium state and leads to convergence. (v) The effective size of successful agents is reduced recursively while progressing through different states to further facilitate the speed of convergence and decrease the number of features. (vi) The performance comparison with state-of-the-art feature selection methods shows a significant improvement and promise of the proposed method over the existing methods.

An affordable detection system based on RT-LAMP and DNA-nanoprobes for avian metapneumovirus

Airborne animal viral pathogens can rapidly spread and become a global threat, resulting in substantial socioeconomic and health consequences. To prevent and control potential epidemic outbreaks, accurate, fast, and affordable point-of-care (POC) tests are essential. As a proof-of-concept, we have developed a molecular system based on the loop-mediated isothermal amplification (LAMP) technique for avian metapneumovirus (aMPV) detection, an airborne communicable agent mainly infecting turkeys and chickens. For this purpose, a colorimetric system was obtained by coupling the LAMP technique with specific DNA-functionalized AuNPs (gold nanoparticles). The system was validated using 50 different samples (pharyngeal swabs and tracheal tissue) collected from aMPV-infected and non-infected chickens and turkeys. Viral detection can be achieved in about 60 min with the naked eye, with 100% specificity and 87.88% sensitivity for aMPV. In summary, this novel molecular detection system allows suitable virus testing in the field, with accuracy and limit of detection (LOD) values highly close to qRT-PCR-based diagnosis. Furthermore, this system can be easily scalable to a platform for the detection of other viruses, addressing the current gap in the availability of POC tests for viral detection in poultry farming.Key points•aMPV diagnosis using RT-LAMP is achieved with high sensitivity and specificity.•Fifty field samples have been visualized using DNA-nanoprobe validation.•The developed system is a reliable, fast, and cost-effective option for POCT.Graphical

Evolution of the UCC+i as a dynamic agent of science communication. Professional perceptions of their structure and future challenges in the digital context

The Units of Scientific Culture and Innovation (UCC+i) have become one of the most dynamic agents in the dissemination of science in Spain. Using our own methodology based on a survey of almost 60 professionals, four key aspects of their professional structure are analysed exhaustively: main functions, institutional location, evolution of the human resources available to them, and the professional recognition given to them. After contextualising the origin, evolution, and current map of the UCC+i, the results indicate that these units specialise mainly in the communication of results and the dissemination of science. And, to a lesser extent, they perform advisory and training functions. At the institutional level, they are placed under the supervision of the communication and research areas. Regarding staffing, the most common structure is that of three professionals, but with little expectation of expansion and limited recognition by the universities themselves. In short, their high professional visibility is identified as their main strong point, and the lack of economic resources as a serious problem for their development.

An effective approach for reducing data redundancy in multi-agent system communication

The redundancy of the data is an active research topic. While an agent works in a multi-agent system, the number of messages between them increases. This is due to the fact that the functionalities data depends on other agents in terms of functional requirements. Typically, only one agent in a multi-agent system is responsible for accessing a database instead of replicating the database on each agent. A database is stored on multiple agents rather than a single agent to avoid a single point of failure. In this approach, the system has a higher load because one agent is responsible for all agent queries and must send duplicate messages to multiple agents, resulting in redundant data. In this research, we present Multi-Agent System for Commodity Data (MASCD) framework, the multi-agent system based communication using the distributed hash system, to reduce data redundancy in multi-agent system communication. Our anticipated method demonstrated how we divided the database names and efficiently distributed data to each agent. The database splitting is based on manufacturer names or product names. We utilize a table based on prime numbers. Through the hash function, we ascertain the index of the agent granted access to the relevant data. Each agent is accountable for its data. We use a Distributed Hash Table for efficient querying that stores data as key-value pairs. Each agent maintains a Finger Table containing the next and previous nodes for agent communication purposes. Using FIPA messages, we demonstrated how an agent could interact optimally. In conclusion, we illustrate the application of the proposed approach through a case study of mobile phones and university information systems.

Nitrogen-mediated volatilisation of defensive metabolites in tomato confers resistance to herbivores.

Plants synthesise a vast array of volatile organic compounds (VOCs), which serve as chemical defence and communication agents in their interactions with insect herbivores. Although nitrogen (N) is a critical resource in the production of plant metabolites, its regulatory effects on defensive VOCs remain largely unknown. Here, we investigated the effect of N content in tomato (Solanum lycopersicum) on the tobacco cutworm (Spodoptera litura), a notorious agricultural pest, using biochemical and molecular experiments in combination with insect behavioural and performance analyses. We observed that on tomato leaves with different N contents, S. litura showed distinct feeding preference and growth and developmental performance. Particularly, metabolomics profiling revealed that limited N availability conferred resistance upon tomato plants to S. litura is likely associated with the biosynthesis and emission of the volatile metabolite α-humulene as a repellent. Moreover, exogenous application of α-humulene on tomato leaves elicited a significant repellent response against herbivores. Thus, our findings unravel the key factors involved in N-mediated plant defence against insect herbivores and pave the way for innovation of N management to improve the plant defence responses to facilitate pest control strategies within agroecosystems.

Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent’s decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network.

Portray from "him" to "her": Case Study on Media Practices and Practical Significance of Chinese Female-themed Bloggers

The prosperity of new media and self-media has empowered women with more discourse power and influence in self-expression and group advocacy, making female-themed bloggers the emerging agents of gender communication. Through content analysis and text analysis, this paper explores the discourse expression and public influence of female-themed bloggers on social media platforms represented by "Little Red Book." It is found that female-themed bloggers have achieved considerable communication effects by shaping a high sense of social responsibility, ingenious topic planning and issue construction, creative narrative strategies, and text content focusing on women, continuously awakening women's consciousness and promoting gender equality. The article also identifies issues in the immature stage of the female-themed communication ecosystem, uneven content quality, and evident information cocoons, and provides corresponding recommendations.

Revolutionizing Student Accommodation: A MERN-Based Rental System For Seamless Property Management

This web application facilitates the registration of individual homes or apartments to streamline the process of finding ideal rental properties. Users can conveniently search for their next rental in targeted areas through a user-friendly interface. The platform is designed to cater to diverse real estate needs in India, including property buying, selling, and leasing. It serves as a valuable tool for investment by maintaining a comprehensive database of various properties and agent information. Not only does it assist users in accessing updated property information globally, but it also enables agents to manage and update their listings. The platform alleviates the complexities associated with traditional property search methods by offering a simplified online form, minimizing the need for individual agent communication, appointment scheduling, and time coordination

A transformative shift in urban ecology toward a more active and relevant future for the field and for cities

This paper builds on the expansion of urban ecology from a biologically based discipline—ecology in the city—to an increasingly interdisciplinary field—ecology of the city—to a transdisciplinary, knowledge to action endeavor—an ecology for and with the city. We build on this “prepositional journey” by proposing a transformative shift in urban ecology, and we present a framework for how the field may continue this shift. We conceptualize that urban ecology is in a state of flux, and that this shift is needed to transform urban ecology into a more engaged and action based field, and one that includes a diversity of actors willing to participate in the future of their cities. In this transformative shift, these actors will engage, collaborate, and participate in a continuous spiral of knowledge → action → knowledge spiral and back to knowledge loop, with the goal of co producing sustainable and resilient solutions to myriad urban challenges. Our framework for this transformative shift includes three pathways: (1) a repeating knowledge → action → knowledge spiral of ideas, information, and solutions produced by a diverse community of agents of urban change working together in an “urban sandbox”; (2) incorporation of a social–ecological–technological systems framework in this spiral and expanding the spiral temporally to include the “deep future,” where future scenarios are based on a visioning of seemingly unimaginable or plausible future states of cities that are sustainable and resilient; and (3) the expansion of the spiral in space, to include rural areas and places that are not yet cities. The three interrelated pathways that define the transformative shift demonstrate the power of an urban ecology that has moved beyond urban systems science and into a realm where collaborations among diverse knowledges and voices are working together to understand cities and what is urban while producing sustainable solutions to contemporary challenges and envisioning futures of socially, ecologically, and technologically resilient cities. We present case study examples of each of the three pathways that make up this transformative shift in urban ecology and discuss both limitations and opportunities for future research and action with this transdisciplinary broadening of the field.

A Decentralized Multi-Agent Path Planning Approach Based on Imitation Learning and Selective Communication

Abstract Multi-agent path planning (MAPP) is crucial for large-scale mobile robot systems to work safely and properly in complex environments. Existing learning-based decentralized MAPP approaches allow each agent to gather information from nearby agents, leading to more efficient coordination among agents. However, these approaches often struggle with reasonably handling local information inputs for each agent, and their communication mechanisms between agents need to be further refined to treat those congested traffic scenarios effectively. To address these issues, we propose a decentralized MAPP approach based on imitation learning and selective communication. Our approach adopts an imitation learning architecture that enables agents to rapidly learn complex behaviors from expert planning experience. The information extraction layer is integrated with convolutional neural network (CNN) and gated recurrent unit (GRU) for capturing features from local field-of-view observations. A two-stage selective communication process based on graph attention neural network (GAT) is developed to reduce the required neighbor agents in inter-agent communication. In addition, an adaptive strategy switching mechanism utilizing local expert-planned paths is designed to support robots to escape from local traps. The effectiveness of our proposed approach is evaluated in simulated grid environments with varying map sizes, obstacle densities, and numbers of agents. Experimental results show that our approach outperforms other decentralized path planning methods in success rate while maintaining the lowest flowtime variation and communication frequency. Furthermore, our approach is computationally efficient and scalable, making it suitable for real-world applications.

Review of the Mathematical Model, Properties, Classes and other Features of Software Agent Development

Reviewed: features of the definition of an agent and a software agent, its dimensions and other components; models of software agents and its properties; classification of software agents by architecture, communication principles and agent communication languages (ACL), as well as existing platforms for their development (e.g., JADE, SPADE); multi-agent system (MAS); behavior types of SPADE software agent based on the example of one of the platforms (including for subsequent experiments to compare behaviors in terms of hardware resources usage). Minor adjustments have been made to the syntax of mathematical expressions describing the agent model, and a revision of the formalized definitions of agent property set has been proposed; a formalized description of the model of studied agent type is determined.

How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents.

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent-based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long-held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well-connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation.

Proteomic Profiling of Plasma- and Gut-Derived Extracellular Vesicles in Obesity.

Obesity entails metabolic alterations across multiple organs, highlighting the role of inter-organ communication in its pathogenesis. Extracellular vesicles (EVs) are communication agents in physiological and pathological conditions, and although they have been associated with obesity comorbidities, their protein cargo in this context remains largely unknown. To decipher the messages encapsulated in EVs, we isolated plasma-derived EVs from a diet-induced obese murine model. Obese plasma EVs exhibited a decline in protein diversity while control EVs revealed significant enrichment in protein-folding functions, highlighting the importance of proper folding in maintaining metabolic homeostasis. Previously, we revealed that gut-derived EVs' proteome holds particular significance in obesity. Here, we compared plasma and gut EVs and identified four proteins exclusively present in the control state of both EVs, revealing the potential for a non-invasive assessment of gut health by analyzing blood-derived EVs. Given the relevance of post-translational modifications (PTMs), we observed a shift in chromatin-related proteins from glycation to acetylation in obese gut EVs, suggesting a regulatory mechanism targeting DNA transcription during obesity. This study provides valuable insights into novel roles of EVs and protein PTMs in the intricate mechanisms underlying obesity, shedding light on potential biomarkers and pathways for future research.

Emerging Role of Extracellular Vesicles in Intercellular Communication in the Brain: Implications for Neurodegenerative Diseases and Therapeutics.

Extracellular vesicles (EVs) are minute lipid-bilayer sacs discharged by cells, encompassing a diverse array of proteins, nucleic acids, and lipids. The identification of EVs as pivotal agents in intercellular communication has sparked compelling research pathways in the realms of cell biology and neurodegenerative diseases. Utilizing EVs for medicinal reasons has garnered interest due to the adaptability of EV-mediated communication. EVs can be classified based on their physical characteristics, biochemical composition, or cell of origin following purification. This review delves into the primary sub-types of EVs, providing an overview of the biogenesis of each type. Additionally, it explores the diverse environmental conditions triggering EV release and the originating cells, including stem cells and those from the Central Nervous System. Within the brain, EVs play a pivotal role as essential mediators of intercellular communication, significantly impacting synaptic plasticity, brain development, and the etiology of neurological diseases. Their potential diagnostic and therapeutic applications in various brain-related conditions are underscored, given their ability to carry specific cargo. Specially engineered EVs hold promise for treating diverse diseases, including neurodegenerative disorders. This study primarily emphasizes the diagnostic and potential therapeutic uses of EVs in neurological disorders such as Alzheimer's Disease, Huntington's Disease, Parkinson's Disease, Amyotrophic Lateral Sclerosis, and Prions disease. It also summarizes innovative techniques for detecting EVs in the brain, suggesting that EVs could serve as non-invasive biomarkers for early detection, disease monitoring, and prognosis in neurological disorders.

Computational philosophy: reflections on the PolyGraphs project

In this paper, we situate our computational approach to philosophy relative to other digital humanities and computational social science practices, based on reflections stemming from our research on the PolyGraphs project in social epistemology. We begin by describing PolyGraphs. An interdisciplinary project funded by the Academies (BA, RS, and RAEng) and the Leverhulme Trust, it uses philosophical simulations (Mayo-Wilson and Zollman, 2021) to study how ignorance prevails in networks of inquiring rational agents. We deploy models developed in economics (Bala and Goyal, 1998), and refined in philosophy (O’Connor and Weatherall, 2018; Zollman, 2007), to simulate communities of agents engaged in inquiry, who generate evidence relevant to the topic of their investigation and share it with their neighbors, updating their beliefs on the evidence available to them. We report some novel results surrounding the prevalence of ignorance in such networks. In the second part of the paper, we compare our own to other related academic practices. We begin by noting that, in digital humanities projects of certain types, the computational component does not appear to directly support the humanities research itself; rather, the digital and the humanities are simply grafted together, not fully intertwined and integrated. PolyGraphs is notably different: the computational work directly supports the investigation of the primary research questions, which themselves belong decidedly within the humanities in general, and philosophy in particular. This suggests an affinity with certain projects in the computational social sciences. But despite these real similarities, there are differences once again: the computational philosophy we practice aims not so much at description and prediction as at answering the normative and interpretive questions that are distinctive of humanities research.