Human Agents Research Articles

Abstract C111: A novel human monoclonal antibody targeting active ADAM10 demonstrates anti-tumor effects in colorectal cancer

Abstract Colorectal cancer (CRC) is the third most diagnosed cancer in the USA and accounts for more than 600,000 deaths annually worldwide, primarily due to relapse with highly aggressive, chemo-resistant disease characterized by poorly differentiated cancer cells with stem cell-like properties. A common signature of these chemo-resistant tumors is dysregulation of Notch receptor signaling, as well as upregulation of its metalloprotease activator, ADAM10. Although small molecule inhibition of either Notch or ADAM10 has been shown to produce potent anti-tumor effects, these therapeutic strategies have failed in clinical trials primarily due to systemic toxicities, especially cytotoxic effects on the gastrointestinal tract and musculoskeletal system, highlighting the need for development of more targeted approaches. We have previously demonstrated that ADAM10 predominantly exists in an inhibited state in normal tissues but is activated in tumor cells through a conformational change in the extracellular domain, thus providing a potential target for tumor-specific modulation of ADAM10 activity. Here, we look to characterize a novel human monoclonal antibody agent (1H5) that selectively targets an exposed extracellular region of activated ADAM10 on tumor cells. We previously demonstrated that treatment with a murine version of the antibody (8C7), specific for the activated form of both mouse and human ADAM10, conferred a significant reduction in tumor burden against human CRC cell lines in cell culture and transplants in xenograft models, and relapse was prevented when 8C7 was combined with chemotherapy. Here, we demonstrate that the 1H5 monoclonal antibody effectively reduces cell viability in numerous colorectal cancer cell lines in vitro and induces marked tumor regression in established xenograft models through inhibition of Notch signaling. Through the use of additional genetic tools combined with in vitro and in vivo models, our preclinical study aims to fully evaluate 1H5 as novel therapeutic agent to treat aggressive colorectal cancer. We aim to propel this novel agent toward clinical-stage development. Citation Format: Pargol Mashati, Dan Sun, Eduardo Garcia Reino, Jessica A. Blandino, Ben Mason, Nayanendu Saha, Dimitar B. Nikolov, Prem Premsrirut. A novel human monoclonal antibody targeting active ADAM10 demonstrates anti-tumor effects in colorectal cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C111.

Interpretable Learned Emergent Communication for Human–Agent Teams

Learning interpretable communication is essential for multi-agent and human-agent teams (HATs). In multi-agent reinforcement learning for partially-observable environments, agents may convey information to others via learned communication, allowing the team to complete its task. Inspired by human languages, recent works study discrete (using only a finite set of tokens) and sparse (communicating only at some time-steps) communication. However, the utility of such communication in human-agent team experiments has not yet been investigated. In this work, we analyze the efficacy of sparse-discrete methods for producing emergent communication that enables high agent-only and human-agent team performance. We develop agent-only teams that communicate sparsely via our scheme of Enforcers that sufficiently constrain communication to any budget. Our results show no loss or minimal loss of performance in benchmark environments and tasks. In human-agent teams tested in benchmark environments, where agents have been modeled using the Enforcers, we find that a prototype-based method produces meaningful discrete tokens that enable human partners to learn agent communication faster and better than a one-hot baseline. Additional HAT experiments show that an appropriate sparsity level lowers the cognitive load of humans when communicating with teams of agents and leads to superior team performance.

Exploring the impacts of using the artificial intelligence voice-enabled chatbots on customers interactions in the United Arab Emirates

<p>Over the past decade, chatbots have experienced a significant increase in<br />popularity, especially since the outbreak of COVID-19. In the United Arab<br />Emirates, most businesses have accelerated their digital transformation and<br />are relying on chatbots as a primary way to interact with customers. However,<br />many of these chatbots lack a voice input option for customers. This study<br />investigates the benefits and challenges of incorporating artificial intelligence<br />(AI) voice-enabled chatbots into United Arab Emirates (UAE) businesses and<br />how this impacts customer experience. Qualitative research techniques were<br />used to gather information, and the results demonstrate that implementing AI<br />chatbots with voice input and sentiment analysis features can enhance the<br />customer experience by making it more efficient and convenient.<br />Additionally, the study found that AI chatbots can ultimately save businesses<br />time and money, and while they may reduce the need for human agents, they<br />will not replace them entirely. Finally, an implementation framework and<br />suggestions are provided for businesses that are interested in adopting AI<br />voice-enabled chatbots for customer interactions.</p>

Phytochemicals in Bifunctional Roles: From Defense Molecules in Plants to Therapeutic Agents in Humans

Phytochemicals in Bifunctional Roles: From Defense Molecules in Plants to Therapeutic Agents in Humans

Bots for mental health: the boundaries of human and technology agencies for enabling mental well-being within organizations

PurposeThe importance of mental wellbeing and the need for organizations to address it is increasing in the post-pandemic context. Although Artificial Intelligence (AI) is increasingly being adopted in HRM functions, its adoption and utility for enabling mental wellbeing is limited. Building on the Open System Theory (OST) and adopting the technology-in-practice lens, the authors examined the roles of human and technology agencies in enabling mental wellbeing.Design/methodology/approachThe study was conducted in two stages; in Stage 1, the authors adopted a case methodology approach to examine the feasibility of a technology company's offerings to assess mental wellbeing. In Stage 2, the authors followed the grounded theory approach and interviewed 22 key stakeholders and HR leaders of diverse organizations. The authors used Gioia's approach to analyze the data.FindingsThe study demonstrates the interdependence and inseparability of human activity, technological capability and structured context. Specifically, the authors observe that AI adoption is pushing the boundaries of how organizations could support employees' mental health and wellbeing. These technological advancements and adoption are likely to facilitate the evolution of agentic practices, routines and structures.Research limitations/implicationsThis study carries two important implications. While the advent of cutting-edge technologies appears to affect employees' mental wellbeing, the study findings indicate the assistive role of technology in supporting mental wellbeing and facilitating changes in organizational practices. Second, the ontology of technology-in-practice shows how human–machine agencies gain newer relevance from the interactions that unite them. Specifically, per OST, technology (from an external context) can potentially change how mental wellbeing practices in organizations are managed. The authors extend the existing literature by suggesting that both human agents and internal contexts effectively limit the potential of technology agents to change existing structures significantly.Originality/valueThe authors address the need for more research on the technology-management interface, and the boundaries of technology-enabled wellbeing at work. While AI-HRM scholarship has primarily relied on micro-level psychological theories to examine impact and outcomes, the authors borrow from the macro-level theories, such as the OST and the technology-in-practice to explain how AI is shifting the boundaries of human and machine agencies for enabling mental wellbeing.

RGS⊕\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\oplus $$\\end{document}: RDF graph synchronization for collaborative robotics

In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge is gathered incrementally and in different ways by heterogeneous robots and humans. The purpose of this paper is to describe an RDF Graph Synchronization System called RGS⊕\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\oplus $$\\end{document}. It is assumed that a dynamic set of agents provide or retrieve knowledge stored in their local RDF Graphs which are continuously synchronized between agents. The RGS⊕\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\oplus $$\\end{document} System was designed to handle unreliable communication and does not rely on a static centralized infrastructure. It is capable of synchronizing knowledge as timely as possible and allows agents to access knowledge while it is incrementally acquired. A deeper empirical analysis of the RGS⊕\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\oplus $$\\end{document} System is provided that shows both its efficiency and efficacy.

Contribution of nadR to the cell growth and virulence of Streptococcus suis serotype 2

Streptococcus suis serotype 2 (SS2) has been reported to be a highly invasive pathogen in swine and a zoonotic agent for humans. Although many bacterial virulence factors have been identified, our an insightful understanding of SS2 pathogenicity is lacking. The gene nadR, encoding nicotinamide-nucleotide adenylyltransferase, was first reported as a regulator and transporter of the intracellular NAD synthesis pathway in Salmonella typhimurium. In this study, we constructed a mutant strain of nadR (ΔnadR) to test the phenotypic and virulence variations between the deletion mutant and the wild-type strain ZY05719. The phenotypic experimental results showed that ΔnadR obviously inhibited cell growth and exhibited shorter chains than WT. The growth defect of ΔnadR was caused by the loss of the function of nadR for transporting the substrates nicotinamide mononucleotide and nicotinamide riboside in the intracellular NAD synthesis pathway. In the process of interaction with the host, ΔnadR participated in adherence and invasion to the host cells, and it was more easily cleared by RAW264.7 macrophages. More importantly, both zebrafish and BALB/c mice in vivo virulence experimental results showed that ΔnadR dramatically attenuated the virulence of SS2, and the ability of ΔnadR to colonize tissues was notably reduced in comparison with that of WT in the BALB/c mice infection model. To the best of our knowledge, this is the first time to demonstrate that nadR not only plays an important role in bacterial growth, but also in connection with the virulence of SS2 as a global transcriptional regulator.

Leveraging a visual language for the awareness-based design of interaction requirements in digital twins

User interfaces for digital twins (DTs) should provide information that allows the user to be aware of the state of the physical entity that is virtualised. Typically, this real entity is a shared space in which various human and artificial agents interact (for instance, in smart cities, various citizens and vehicles interact; in manufacturing, operators and machinery cooperate in the production, etc.) and the user interface must provide information about its state. This work presents ADD (Awareness Description Diagrams) as a technique for modelling requirements of Human–DT interaction. A study was conducted in order to virtualise a natural space where groups of hikers carry out their activities. This study assesses the learning curve of ADD, its feasibility to model Human–DT requirements, and its utility to design user interfaces. The results of the study provide valuable insights into the effectiveness of the ADD technique.

Modernity and Disenchantment: Charles Taylor on the Identity of the Modern Self

In his magnum opus, Sources of the Self: The Making of Modern Identity (1989), Charles Taylor gives an exhaustive and teleologically interpretive history of the modern self. He, in fact, is in search of the core of the modern identity. By ‘identity’ Taylor means the ensemble of the understanding of what is to be a ‘human agent’, a ‘person’, a ‘self’. Taylor in generating the ontology of the self is greatly inspired by the understanding of Dasein in Heidegger. This paper also focuses on how Taylor uses Heidegger’s hermeneutics of the self in several ways to give to modernity a base that is not Cartesian. Taylor’s central argument is ‘how the assertion of the modern individual has spawned an erroneous understanding (identity) of the self’, where one experiences a loss of horizon. He has turned our attention, more than anyone else, towards the communitarian constitution of the self, and pointed out the limitations of insights within liberal individualism. For Taylor, as for early Heidegger, the self is not neutral or atomic. The self exists only in terms of questions and constitutive concerns, and it is not amenable to arbitrary determination, but can be made sense of only in terms of its life as a whole at any moment.

Biosynthesis, characterization, and antifungal activity of plant-mediated silver nanoparticles using Cnidium monnieri fruit extract.

The present study describes a novel method for green synthesis of silver nanoparticles using Cnidium monnieri (CM-AgNPs). Cnidium monnieri fruit is an excellent anti tinea drug that can be used externally to treat superficial fungal infections in the human body. The aqueous ethanolic extract of Cnidium monnieri fruit was prepared and employed in the synthesis of stable silver nanoparticles via biological reduction method. The synthesis conditions of CM-AgNPs was systematically optimized using Box-Behnken design. CM-AgNPs were well characterized by UV-spectroscopy and X-ray powder diffraction (XRD), and it was confirmed that the synthesized particles were AgNPs. The possible functional groups required for the reduction and stabilization of CM-AgNPs in the extract were identified through FTIR spectrum. The size of CM-AgNPs structure was confirmed to be approximately 44.6 nm in polydisperse spherical shape through scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser dynamic light scattering (DLS). Further, the minimum inhibitory concentration 90% (MIC90) ratios values of Cm-AgNPs against Trichophyton rubrum (7 d), T. mentagrophytes (7 d) and Candida albicans (24 h) were 3.125, 3.125, and 0.78125 μg/mL, respectively, determined by the broth micro dilution method. Finally, the result was concluded that the synthesized AgNPs could be further evaluated in large scale as a potential human topical antifungal agent.

Understanding responsibility under uncertainty: A critical and scoping review of autonomous driving systems

Autonomous driving systems (ADS) operate in an environment that is inherently complex. As these systems may execute a task without the permission of a human agent, they raise major safety and responsibility issues. To identify the relevant issues for information systems, we conducted a critical and scoping review of the literature from many disciplines. The innovative methodology we used combines bibliometrics techniques, grounded theory and a critical conceptual framework to analyse the structure and research themes of the field. Our findings show that there are certain ironies in the way in which responsibility for apparently safe autonomous systems is apportioned. These ironies are interconnected and reveal that there remains significant uncertainty and ambiguity regarding the distribution of responsibility between stakeholders. The ironies draw attention to the challenges of safety and responsibility with ADS and possibly other cyber-physical systems in our increasingly digital world. We make seven recommendations related to (1) value sensitive design and system theory approaches; (2) stakeholders’ interests and interactions; (3) task allocation; (4) deskilling; (5) controllability; (6) responsibility (moral and legal); (7) trust. We suggest five areas for future IS research on ADS. These areas are related to socio-technical systems, critical research, safety, responsibility and trust.

From Animal Models to Clinical Trials: The Potential of Antimicrobials in Multiple Sclerosis Treatment.

Multiple sclerosis (MS) is a chronic, autoimmune, demyelinating disease of the central nervous system (CNS). Microbes, including bacteria and certain viruses, particularly Epstein-Barr virus (EBV), have been linked to the pathogenesis of MS. While there is currently no cure for MS, antibiotics and antivirals have been studied as potential treatment options due to their immunomodulatory ability that results in the regulation of the immune process. The current issue addressed in this systematic review is the effect of antimicrobials, including antibiotics, antivirals, and antiparasitic agents in animals and humans. We performed a comprehensive search of PubMed, Google Scholar, and Scopus for articles on antimicrobials in experimental autoimmune encephalomyelitis animal models of MS, as well as in people with MS (pwMS). In animal models, antibiotics tested included beta-lactams, minocycline, rapamycin, macrolides, and doxycycline. Antivirals included acyclovir, valacyclovir, and ganciclovir. Hydroxychloroquine was the only antiparasitic that was tested. In pwMS, we identified a total of 24 studies, 17 of them relevant to antibiotics, 6 to antivirals, and 1 relevant to antiparasitic hydroxychloroquine. While the effect of antimicrobials in animal models was promising, only minocycline and hydroxychloroquine improved outcome measures in pwMS. No favorable effect of the antivirals in humans has been observed yet. The number and size of clinical trials testing antimicrobials have been limited. Large, multicenter, well-designed studies are needed to further evaluate the effect of antimicrobials in MS.

Positioning Paradata: A Conceptual Frame for AI Processual Documentation in Archives and Recordkeeping Contexts

The emergence of sophisticated Artificial Intelligence (AI) and machine learning tools poses a challenge to archives and records professionals, who are accustomed to understanding and documenting the activities of human agents rather than the often-opaque processes of sophisticated AI functioning. Preliminary work has proposed the term paradata to describe the unique documentation needs that emerge for archivists using AI tools to process records in their collections. For the purposes of archivists working with AI, paradata is conceptualized here as information recorded and preserved about records’ processing with AI tools; it is a category of data that is defined both by its relationship with other datasets and by the documentary purpose it serves. This article surveys relevant literature across three contexts to scope the relevant scholarship that archivists may draw upon to develop appropriate AI documentation practices. From the statistical social sciences and the visual heritage fields, the article discusses existing definitions of paradata and its ambiguous, often contextually dependent relationship with existing metadata categories. Approaching the problem from a sociotechnical perspective, literature on Explainable Artificial Intelligence (XAI) insists pointedly that explainability be attuned to specific users’ stated needs—needs that archivists may better articulate using the framework of paradata. Most importantly, the article situates AI as a challenge to accountability, transparency, and impartiality in archives by introducing an unfamiliar non-human agency, one that pushes the limits of existing archival practice and demands the development of new concepts and vocabularies to shape future technological and methodological developments in archives.

Specificity of Teaching in the Section «General Epidemiology of Infectious Diseases» for Foreign Students

Teaching the discipline «Epidemiology» to foreign students is associated with certain difficulties due to both the lack of equivalents in the English language of terms and concepts used in Russia and the historically established difference in approaches to studying the occurrence and spread of diseases in human society. Schematically, in English-language literature and textbooks on epidemiology, the process of the occurrence of diseases is described on the model of the epidemiological triangle, in Russian literature – historically on the model of the epidemic process, the founder of the doctrine of which is L.V. Gromashevsky. The epidemiological triad is represented by the interaction of a human host, agent and environment – the basis of the epidemic process are 3 elements: the source of infection, the transmission mechanism and the susceptible organism. At first glance, the model of the epidemic process and the epidemiological triangle are somewhat similar. However, the model of the epidemic process according to L.V. Gromashevsky more clearly indicates the population level of the phenomenon, and the epidemiological triangle pays more attention /emphasis on the properties of the pathogen and its interaction with the human body (the level of organism) in the external environment. Teaching in the section «General epidemiology of infectious diseases» teaching about the epidemic process for foreign students is advisable to build from universal models such as the epidemiological triad, harmoniously combining the epidemiology of infectious and non-communicable diseases, followed by the separation of these concepts and the introduction of specific terminology adopted in Russia (source of infection, epidemic focus, transmission mechanism, transmission pathways). At the same time, along with non- communicable diseases, the processes of the spread of infections associated with the provision of medical care, sapronoses are most significantly illustrated with the help of an epidemiological triangle.

Autonomous Systems and Technology Resistance: New Tools for Monitoring Acceptance, Trust, and Tolerance

AbstractWe introduce the notion of Tolerance for autonomous artificial agents (and its antithetical concept, Intolerance), motivating its theoretical adoption in the fields of social robotics and human—agent interaction, where it can effectively complement two contiguous, but essentially distinct, constructs—Acceptance and Trust—that are broadly used by researchers. We offer a comprehensive conceptual model of Tolerance, construed as a user’s insusceptibility or resilience to Autonomy Estrangement (i.e., the uncanny sense of isolation and displacement experienced by the humans who believe, for right or wrong reasons, that robots can subvert and/or control their lives). We also refer to Intolerance to indicate the opposite property, that is the user’s susceptibility or proneness to Autonomy Estrangement. Thus, Tolerance and Intolerance are inverse representations of the same phenomenological continuum, with Intolerance increasing when Tolerance decreases and vice versa. While Acceptance and Trust measure how the user’s interaction with a particular robot is satisfying and efficacious, the dyad Tolerance/Intolerance reflects how the user’s attitude is affected by deeply held normative beliefs about robots in general. So defined, a low Tolerance (that is a high Intolerance) is expected to correlate to antagonistic responses toward the prospect of adoption: specifically, Intolerant attitudes predict the kind of anxious and hostile behaviours toward Agents that originate from the concerns that autonomous systems could deeply disrupt the lives of humans (affecting their work cultures, ways of living, systems of values, etc.) or dominate them (making humans redundant, undermining their authority, threatening their uniqueness, etc.). Thus, Negative beliefs and worldviews about Agents are the cause of the Intolerant attitude toward Agents, which predicts Autonomy Estrangement, which in turn correlates to low Adoption Propensity and avoidance and rejection behaviours.

A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study

The adoption of innovative technologies confronts IT-Service-Management (ITSM) with an increasing volume and variety of requests. Artificial intelligence (AI) possesses the potential to augment customer service employees. However, the training data for AI systems are annotated by domain experts with little interest in labeling correctly due to their limited perceived value. Ultimately, insufficient labeled data leads to diminishing returns in AI performance. Following a design science research approach, we provide a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures. We develop a functional prototype based on 17,120 support tickets provided by a pilot partner as an instantiation and evaluate the design through accuracy metrics and user evaluations. Our evaluation revealed that recommendations after label improvement showed increased user ratings, and users are willing to contribute their domain knowledge. We demonstrate that our design benefits for both human agent and AI systems in the form of hybrid intelligence service systems. Overall, our results emphasize agents' need for value-in-use by providing better results if they improve the labeling of support tickets pre-labeled by AI. Thus, we provide prescriptive knowledge of a novel HIL design that enables efficient and interactive labeling in the context of diverse applications of reinforcement learning systems.

Prey body size generates bias for human and avian agents: Cautions for interpreting small game assemblages

Identifying the accumulating agent(s) of small animal assemblages at archaeological sites is challenging given the range of behaviors, skeletal plans, and body sizes of prey species as well as the range of potential predators. Although mammalian carnivores, raptors, and humans are considered typical predators of small game, there are many contexts—especially in island ecosystems—where humans and raptors are the primary agents for small animal assemblages in the absence of mammalian carnivores. Moreover, smaller animals tend to range in body sizes in these geographically restricted ecosystems. This study aims to broadly explore: 1) how small animal body size affects the taphonomic signature(s) of human and avian predation; and 2) how human and avian-generated small game assemblages compare with one another. To investigate these questions, this study compared the resulting taphonomic patterns of small animal predation by raptors and humans using data collected during a controlled-feeding experiment and an observational study, respectively. In the controlled-feeding experiment, deceased laboratory rats (Rattus norvegicus domestica) (50–500 g) were fed to two Milky Eagle Owls (Ketupa lacteus), one King Vulture (Sarcoramphus papa), and two Lappet-Faced Vultures (Torgos tracheliotos). In the observational study, we explored how humans processed various species of small prey ranging in body mass (80–5000 g) by analyzing anthropogenic modifications (i.e., cutmarks, tooth marks, burning). The resulting small animal assemblages were analyzed post-consumption for skeletal element representation, fragmentation and breakage patterns, and bone surface modifications. Results show that small prey body size significantly affects pre-depositional patterning in terms of skeletal element survival, butchering intensities, and cutmark frequencies. Furthermore, our experimentally generated assemblages show that breakage, fragmentation, and element relative abundance patterns can be used to distinguish between human and avian-accumulated assemblages in the archaeological record.

Minding the source: toward an integrative theory of human–machine communication

Abstract According to the computers are social actors (CASA) paradigm, a dominant theoretical framework for research on human–computer interaction, people treat computers as if they were people. Recent studies on human–machine communication (HMC) and human–artificial intelligence (AI) interaction, however, appear to focus on when and how people respond to machines differently than to human agents. To reconcile this apparent contradiction, this study reviews critically the two overarching theoretical explanations proposed and tested in each respective tradition, the mindlessness account and the machine heuristic. After elaborating on several conceptual and operational issues with each explanatory mechanism, an alternative theoretical model of HMC is proposed that integrates both research traditions and generates predictions that potentially deviate from the dual-process models. Lastly, it is discussed how recent developments in AI technology invite modifications to the current understanding of HMC and beyond.

Multi-Agent Systems Requirements Analysis for Patient-centered Healthcare Consultancy Service

Agents are currently being discussed in nearly every domain of science and engineering. Through the utilization of IoT and agent-based systems, remote consultations and virtual doctors can provide essential healthcare services by analyzing patient data and medical history. The purpose of this paper is provide an analysis the requirements for multi-agent systems in patient-centered health consultancy services in Kenya. It focuses on various agent types, including hardware, hospital, human, software, database/storage, and technological/framework agents. For hardware, computers, laptops, and mobile devices need specific processing power, memory, network connectivity, storage, and security. Medical equipment must prioritize accuracy, reliability, integration with records, and regulatory compliance. Networking requires high-speed internet, security, and redundancy. Hospital agents offer comprehensive inpatient and outpatient care, categorized into general, mental health, and specialized hospitals. Human agents, like medical specialists, IT professionals, and administrators, play essential roles in healthcare consultancy. Software agents encompass Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), Telemedicine platforms, and more. Database/storage agents include EHRs, Health Information Exchange (HIE) systems, and data encryption. Technological/framework agents involve health information systems, telemedicine platforms, decision support systems, and health data analytics frameworks. These agents enable efficient data management, remote care, evidence-based decisions, and secure data exchange.

Behavior Learning System for Robot Soccer Using Neural Network

With technological developments, the prospect of a human-robot symbiotic society has emerged. A soccer game has characteristics similar to those expected in such a society. Soccer is a multiagent game in which the strategy employed depends on each agent’s position and actions. This paper discusses the results of the development of a learning system that uses a self-organizing map to select behaviors depending on the scenario (two-dimensional absolute coordinates of the agent, other agents, and the ball). The system can reproduce the action-selection algorithms of all the players on a certain team, and the robot can instantly select the next cooperative action from information obtained during the game. Thus, common-sense rules can be shared to learn an action-selection algorithm for a set of both human and robot agents.