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.

A Multi-Agent System for Service Provisioning in an Internet-of-Things Smart Space Based on User Preferences.

The integration of the Internet of Things (IoT) and artificial intelligence (AI) is critical to the advancement of ambient intelligence (AmI), as it enables systems to understand contextual information and react accordingly. While many solutions focus on user-centric services that provide enhanced comfort and support, few expand on scenarios in which multiple users are present simultaneously, leaving a significant gap in service provisioning. To address this problem, this paper presents a multi-agent system in which software agents, aware of context, advocate for their users' preferences and negotiate service settings to achieve solutions that satisfy everyone, taking into account users' flexibility. The proposed negotiation algorithm is illustrated through a smart lighting use case, and the results are analyzed in terms of the concrete preferences defined by the user and the selected settings resulting from the negotiation in regard to user flexibility.

A Survey of Conversational Agents and Their Applications for Self-Management of Chronic Conditions.

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.

Trusting Automation: Designing for Responsivity and Resilience.

OBJECTIVE: This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments. BACKGROUND: Two recent trends-the development of increasingly capable automation and the flattening of organizational hierarchies-suggest a reframing of trust in automation is needed. METHOD: Many publications related to human trust and human-automation interaction were integrated in this narrative literature review. RESULTS: Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity. CONCLUSION: This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design. APPLICATION: A conceptual model comprising four concepts-situation, semiotics, strategy, and sequence-can guide future trust research and design for automation responsivity and more resilient human-automation systems.

Voice Over Body? Older Adults' Reactions to Robot and Voice Assistant Facilitators of Group Conversation.

Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent. Two agent forms with the same underlying artificial intelligence (AI) and voice system were compared: a humanoid robot and a voice assistant. One preliminary study (total n = 24) and one experimental study comparing voice and body morphologies (n = 36) were conducted with older adults and an experienced human facilitator. Findings revealed that the artificiality of the agent, regardless of its form, was beneficial for the socially uncomfortable task of conversation facilitation. Even so, talkative personality types had a poorer experience with the "bodied" robot version. Design implications and supplementary reactions, especially to agent voice, are also discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s12369-022-00925-7.

Multifunctional Fiber-Enabled Intelligent Health Agents.

The application of wearable devices is promoting the development toward digitization and intelligence in the field of health. However, the current smart devices centered on human health have disadvantages such as weak perception, high interference degree, and unfriendly interaction. Here, an intelligent health agent based on multifunctional fibers, with the characteristics of autonomy, activeness, intelligence, and perceptibility enabling health services, is proposed. According to the requirements for healthcare in the medical field and daily life, four major aspects driven by intelligent agents, including health monitoring, therapy, protection, and minimally invasive surgery, are summarized from the perspectives of materials science, medicine, and computer science. The function of intelligent health agents is realized through multifunctional fibers as sensing units and artificial intelligence technology as a cognitive engine. The structure, characteristics, and performance of fibers and analysis systems and algorithms are reviewed, while discussing future challenges and opportunities in healthcare and medicine. Finally, based on the above four aspects, future scenarios related to health protection of a person's life are presented. Intelligent health agents will have the potential to accelerate the realization of precision medicine and active health.

Taxi services and the carsharing alternative: a case study of valencia city.

The public's awareness of pollution in cities is growing. The decrease of carbon dioxide emissions from the use of fossil-fuel-powered cars stands out among the different viable alternatives. To this purpose, more sustainable options, such as carsharing fleets, could be used to replace private automobiles and other services such as taxis. This type of vehicle, which is usually electric, is becoming more common in cities, providing a green mobility option. In this research, we use multi-agent simulations to examine the efficiency of the current taxi fleet in Valencia. After that, we evaluate various carsharing fleet arrangements. Our findings demonstrate the possibility for a mix of the two types of fleets to meet present demand while also improving the city's sustainability.

A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms.

Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a "UUV-task" matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.

Knowledge Gaps: A Challenge for Agent-Based Automatic Task Completion.

The study of human cognition and the study of artificial intelligence (AI) have a symbiotic relationship, with advancements in one field often informing or creating new work in the other. Human cognition has many capabilities modern AI systems cannot compete with. One such capability is the detection, identification, and resolution of knowledge gaps (KGs). Using these capabilities as inspiration, we examine how to incorporate detection, identification, and resolution of KGs in artificial agents. We present a paradigm that enables research on the understanding of KGs for visual-linguistic communication. We leverage and enhance and existing KG taxonomy to identify possible KGs that can occur for visual question answer (VQA) tasks and use these findings to develop a classifier to identify questions that could be engineered to contain specific KG types for other VQA datasets. Additionally, we examine the performance of different VQA models through the lens of KGs.

Modeling of an irrigation system in a virtual physical space.

One of the major challenges that smart agriculture is expected to address is the efficient use of water resources. The conservation and the efficient use of clean water is a long-term strategy worldwide. Modeling of smart agriculture systems is an important factor because the processes there are very slow and sometimes it takes a year or more for a full crop cycle. At the same time, a large amount of data is usually needed to make informed decisions. This determines the importance of developing appropriate systems through which to simulate, generate, optimize and analyze various possible scenarios and prepare appropriate plans. In this paper, an infrastructure known as Virtual-Physical Space adapted for agriculture is presented. The space supports integration of the virtual and physical worlds where analysis and decision making are done in the virtual environment and the state of the physical objects (things) of interest is also taken into account at the same time. Special attention is paid to the possibilities for modeling an irrigation system. An ambient-oriented approach has been adopted, using the Calculus of Context-aware Ambients formalism as the basic tool for modeling agriculture processes. Furthermore, the supporting platform is briefly presented. Active components of the platform are implemented as intelligent agents known as assistants. Users (agriculture operators) are serviced by personal assistants. Currently, the presented modeling system is deployed over a two layered system infrastructure in the region of Plovdiv city. Plovdiv is the center of vegetable production in Bulgaria. The process of modeling intelligent irrigation systems and the current results are discussed in this paper.

Digital Twin Generation: Re-Conceptualizing Agent Systems for Behavior-Centered Cyber-Physical System Development.

Cyber-Physical Systems (CPS) form the new backbone of digital ecosystems. Upcoming CPS will be operated on a unifying basis, the Internet of Behaviors (IoB). It features autonomous while federated CPS architectures and requires corresponding behavior modeling for design and control. CPS design and control involves stakeholders in different roles with different expertise accessing behavior models, termed Digital twins. They mirror the physical CPS part and integrate it with the digital part. Representing role-specific behaviors and provided with automated execution capabilities Digital twins facilitate dynamic adaptation and (re-)configuration. This paper proposes to conceptualize agent-based design for behavior-based Digital twins through subject-oriented models. These models can be executed and, thus, increase the transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate the configuration of CPS. Subject-oriented runtime support enables dynamic adaptation and the federated use of CPS components.

Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles.

This paper presents the results of the design, simulation, and implementation of a virtual vehicle. Such a process employs the Unity videogame platform and its Machine Learning-Agents library. The virtual vehicle is implemented in Unity considering mechanisms that represent accurately the dynamics of a real automobile, such as motor torque curve, suspension system, differential, and anti-roll bar, among others. Intelligent agents are designed and implemented to drive the virtual automobile, and they are trained using imitation or reinforcement. In the former method, learning by imitation, a human expert interacts with an intelligent agent through a control interface that simulates a real vehicle; in this way, the human expert receives motion signals and has stereoscopic vision, among other capabilities. In learning by reinforcement, a reward function that stimulates the intelligent agent to exert a soft control over the virtual automobile is designed. In the training stage, the intelligent agents are introduced into a scenario that simulates a four-lane highway. In the test stage, instead, they are located in unknown roads created based on random spline curves. Finally, graphs of the telemetric variables are presented, which are obtained from the automobile dynamics when the vehicle is controlled by the intelligent agents and their human counterpart, both in the training and the test track.

The STAR-C Intelligent Coach: A Cross-Disciplinary Design Process of a Behavior Change Intervention in Primary Care.

A broad range of aspects are needed to be taken into consideration in the design and development of personalized coaching systems based on artificial intelligence methodologies. This research presents the initial phase of joining different professional and stakeholder perspectives on behavior change technologies into a flexible design proposal for a digital coaching system. The diversity and sometimes opposed views on content, behavior, purposes and context were managed using a structured argument-based design approach, which also feed into the behavior of the personalized system. Results include a set of personalization strategies that will be further elaborated with the target user group to manage sensitive issues such as ethics, social norms, privacy, motivation, autonomy and social relatedness.

The behavioral interaction of road users in traffic: An example of the potential of intelligent agent-based simulations in psychology / La interacción conductal de conductores en el tránsito: un ejemplo sobre el potencial de las simulaciones basadas en agentes inteligentes para la psicología

This paper illustrates what psychologists can do and learn from the use of intelligent agent-based simulations, in combination with experimental designs, in order to model the behavioral interaction of motorists and motorcyclists in urban traffic, when motorcyclists ride in between the lanes of slow-moving or stopped vehicles. The results of the computer model were validated through a measure that estimates its agreement with the results of real-life traffic videos analyses. Lastly, this paper discusses the implications of adopting intelligent agent-based simulations in experimental psychology.

Con un ejemplo se ilustra lo que los psicólogos pueden hacer y aprender al usar las simulaciones basadas en agentes inteligentes combinadas con diseños experimentales para modelar la interacción conductual de automovilistas y motociclistas en el tráfico urbano, cuando estos circulan entre los carriles de vehículos detenidos o circulando a baja velocidad. Los resultados del modelo computacional se validaron con una medida que estima su concordancia con los resultados obtenidos en análisis de videos de tráfico real. El artículo finaliza discutiendo las implicaciones que tiene para la psicología experimental la adopción de las simulaciones basadas en agentes inteligentes.

A Systematic Literature Review of Agents Applied in Healthcare.

Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009-2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications.

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

Recommendations for the ethical use and design of artificial intelligent care providers.

OBJECTIVE: This paper identifies and reviews ethical issues associated with artificial intelligent care providers (AICPs) in mental health care and other helping professions. Specific recommendations are made for the development of ethical codes, guidelines, and the design of AICPs. METHODS: Current developments in the application of AICPs and associated technologies are reviewed and a foundational overview of applicable ethical principles in mental health care is provided. Emerging ethical issues regarding the use of AICPs are then reviewed in detail. Recommendations for ethical codes and guidelines as well as for the development of semi-autonomous and autonomous AICP systems are described. The benefits of AICPs and implications for the helping professions are discussed in order to weigh the pros and cons of their use. RESULTS: Existing ethics codes and practice guidelines do not presently consider the current or the future use of interactive artificial intelligent agents to assist and to potentially replace mental health care professionals. AICPs present new ethical issues that will have significant ramifications for the mental health care and other helping professions. Primary issues involve the therapeutic relationship, competence, liability, trust, privacy, and patient safety. Many of the same ethical and philosophical considerations are applicable to use and design of AICPs in medicine, nursing, social work, education, and ministry. CONCLUSION: The ethical and moral aspects regarding the use of AICP systems must be well thought-out today as this will help to guide the use and development of these systems in the future. Topics presented are relevant to end users, AI developers, and researchers, as well as policy makers and regulatory boards.

Uma arquitetura para troca de mensagens no cuidado de saúde pervasivo baseada no uso de agentes inteligentes / An architecture for message exchange in pervasive healthcare based on the use of intelligent agents

Objetivos: Este trabalho propoe uma arquitetura para a troca de mensagens ciente de contexto em ambientes de Cuidado de Saude Pervasivo. Materiais e métodos: No Cuidado de Saude Pervasivo, novas tecnologias de informacao e comunicacao sao aplicadas para apoiar a prestacao de servicos de saude em qualquer lugar, a qualquer momento e a qualquer pessoa. Tecnologias de Computacao Ubiqua possibilitam a troca de informacao eficiente e segura entre cuidadores de saude e seus pacientes em hospitais, lares e comunidades. Uma vez que os sistemas de informacao de saude disponibilizam os registros de saude em varios formatos eletronicos, a Fundacao openEHR propos um modelo dual para garantir a interoperabilidade semantica entre esses sistemas. Agente Inteligente e uma tecnologia que foi aplicada para simular habilidades humanas em procedimentos de saude. Resultados: Esta arquitetura foi demonstrada e avaliada em um experimento controlado que foi realizado no departamento de Cardiologia de um hospital localizado na cidade de Marilia (Sao Paulo, Brasil). Conclusão: Um aplicativo foi desenvolvido para avaliar essa arquitetura, e os resultados mostraram que a arquitetura e apropriada para facilitar o desenvolvimento de sistemas informacao de saude, fornecendo recursos genericos e uma poderosa solucao para integracao destes sistemas.

Aims: This paper proposes an architecture for the exchange of context-aware messages in Pervasive Healthcare environments. Materials and methods: In Pervasive Healthcare, novel information and communication technologies are applied to support the provision of health services anywhere, at anytime, and to anyone. Ubiquitous Computing technologies allow efficient and safe information exchange amongst caregivers and their patients in communities, homes and hospitals. Since health systems may offer their health records in various electronic formats, the openEHR foundation has proposed a dual model to achieve semantic interoperability between such systems. Intelligent Agents is a technology that has been applied to simulate human skills in healthcare procedures. This architecture is based on technologies from Ubiquitous Computing and Intelligent Agents, and complies with the openEHR dual model. Results: This architecture was demonstrated and evaluated in a controlled experiment that we conducted in the cardiology department of a hospital located in the city of Marília (São Paulo, Brazil). Conclusion: An application was developed to evaluate this architecture, and the results showed that the architecture is suitable for facilitating the development of healthcare systems by offering generic resources and powerful solution to integrate these systems.