Capabilities Of Agents Research Articles

Epstein-Barr Virus and <i>Helicobacter pylori</i> as Two Main Risk Factors in Gastric Cancer

Microbial and viral pathogens have emerged as pivotal agents in oncogenesis. Research conducted in the last twenty years has significantly enhanced our comprehension of the cancer-causing capabilities of infectious agents. An illustrative instance is gastric cancer (GC), which is closely associated with Helicobacter pylori (H. pylori) and Epstein-Barr virus (EBV) infections. Despite concerted endeavors, GC remains a serious clinical challenge ranking as the fifth most commonly diagnosed cancer worldwide. In 2020, an estimated 768,793 people died from GC in the world. The pathogenicity island (PAI), cagA protein, VacA and other virulence factors in H. pylori and several latency factors such as EBNA-1, LMP-1 and LMP2A in Epstein-Barr virus as well as pattern of gene methylation and EBV and H. pylori co-infection are shown as the main causes of pathogen-related GC. The unique molecular and clinical characteristics associated with EBV and H. pylori in GC, highlight the importance of further understanding their respective roles in GC development and progression. This knowledge may inform future preventive and therapeutic strategies targeting these infectious agents in the context of GC. This review aims to elucidate the mechanisms underpinning EBV and H. pylori-induced carcinogenesis in GC.

Evolution of the “4-D Approach” to Dynamic Vision for Vehicles

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.

Shared storytelling with a virtual learning companion: prospects in child-AI collaboration

Advances in natural language processing techniques enhance the capabilities of conversational agents to process input text and generate fluent responses in various genre. In the educational setting, these conversational agents can function as learning companions to promote peer learning, enhance social interaction, and motivate collaborative behavior. In this paper, we investigate how children collaborate with conversational agents in the context of story construction. We conducted user studies and administered survey forms to collect children’s evaluation of their experience, captured the child-agent conversation logs, and used an observation checklist to monitor the interaction. We analyzed the logs to determine how children respond to varying roles portrayed by the agent and the dialogue moves that motivate collaborative behavior during storytelling. We correlated these with the children’s reported perception and our own observation of the interaction based on performance, collaboration and humanity attributes. Drawing on these results, we identified the roles of conversational agents that align with children’s traits and abilities as storytellers and propose dialogue strategies to facilitate collaborative storytelling. Findings from our study can inform future educational technology designers on the roles of conversational agents as collaborative learning companions who can adapt to the individual needs and preferences of the children.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Learning Autonomous Navigation in Unmapped and Unknown Environments.

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent's exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization.

AI‐induced dehumanization

AbstractRecent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices.

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.

From Oncogenesis to Theranostics: The Transformative Role of PSMA in Prostate Cancer.

Prostate cancer, a leading cause of cancer-related mortality among men, is characterized by complex genetic and epigenetic alterations, dysregulation of oncogenic pathways, and a dynamic tumor microenvironment. Advances in molecular diagnostics and targeted therapies have significantly transformed the management of this disease. Prostate-specific membrane antigen (PSMA) has emerged as a critical biomarker, enhancing the precision of prostate cancer diagnosis and treatment. Theranostics, which integrates PSMA-targeted imaging with radioligand therapies, has shown remarkable efficacy in detecting and treating advanced prostate cancer. By leveraging the dual capabilities of PSMA-based diagnostics and therapeutic agents, theranostics offers a personalized approach that improves patient outcomes. This comprehensive review explores the latest developments in PSMA-targeted theranostics and their impact on the future of prostate cancer management, highlighting key clinical trials and emerging therapeutic strategies.

Human-AI Teamwork Interface Design Using Patterns of Interactions

Increasingly capable Artificial Intelligence (AI) agents are reaching new performance levels and humans need to work effectively with them in a variety of collaborative team structures. Of particular interest is “shared control collaboration”, where actions taken by the team depend on the blended combination of inputs from all team entities. We conducted an experiment where participants played a game and maneuvered a spacecraft while working in concert with an AI agent and simultaneously performing a secondary task. The experiment results (i) show different patterns of interactions across user interface designs, agent capabilities, and participants’ game experience, (ii) underscore the importance of providing information about the shared control collaborative task to humans who are working with a less-capable agent, and (iii) imply the potential of real-time communication about agent’s state to achieve better teamwork. Our findings are expected to be transferable to other shared control collaboration settings, including AI-enabled autopilots.

Implementation of Agent Systems in Big Data Management: Integrating Artificial Intelligence for Data Mining Optimization

Background: The rapid growth of data generated across various domains necessitates advanced methodologies for effective data management and extraction of meaningful insights. Traditional data processing techniques often struggle with the volume, variety, and velocity of big data. The integration of Agent Systems and Artificial Intelligence (AI) presents a promising approach to address these challenges by enhancing the efficiency and effectiveness of data mining processes. Objective: This study aims to explore the implementation of Agent Systems in big data management, focusing on how the integration of AI can optimize data mining operations. By leveraging the capabilities of intelligent agents, we seek to improve the accuracy, speed, and scalability of data analysis. Methods: A hybrid research methodology was employed, combining a systematic literature review with an empirical case study. The literature review analyzed previous research on Agent Systems, AI, and big data management to identify key trends and challenges. The empirical case study involved deploying an AI-integrated Agent System within a large-scale data environment to evaluate its performance. Key performance indicators (KPIs) such as processing time, accuracy, and scalability were measured and analyzed. Results: The findings indicate that the integration of AI within Agent Systems significantly enhances the data mining process. The system demonstrated a reduction in processing time by 40%, an increase in data analysis accuracy by 25%, and improved scalability, handling larger datasets more efficiently compared to traditional methods. These improvements were attributed to the autonomous and adaptive nature of agent systems, which enabled dynamic data processing and real-time decision-making. Conclusion: The study concludes that the implementation of AI-integrated Agent Systems in big data management offers substantial benefits, including optimized data mining performance. This integration facilitates more efficient and effective data analysis, which is crucial for organizations dealing with large volumes of data. Future research should focus on further refining these systems and exploring their application across different sectors.

Human-AI collaboration: Unraveling the effects of user proficiency and AI agent capability in intelligent decision support systems

Artificial intelligence (AI) agents are integral components of modern intelligent decision support systems (IDSS), providing their capability to assist in decision-making processes. Understanding the influence of AI involvement on human responses within these systems is crucial for fostering appropriate reliance on AI advice and ensuring a comfortable user experience. This study delves into the impact of AI agents on decision change, correct rate change, confidence rate change and pleasure rating among users. It explores the main and interactive effects of user proficiency and AI agent capability on user responses. A block-factorial experiment involving 45 participants was conducted. The results indicated that high-capability AI agents were associated with substantial decision change, correct rate change and confidence rate change. Participants with higher proficiency, both subjectively and objectively, exhibited reduced reliance on AI agents for task execution. Notably, the involvement of AI agents led to a slight decline in correct rates for participants of high competence (i.e., participants' independent correct rate exceeds AI agent capability), while still enhancing their confidence levels. Additionally, agents lacking human-like embodiment tended to enhance participants’ pleasure ratings. Individual differences in gender, collaboration experience with AI agent, and stereotype on AI agent capability also shaped the decision changes and pleasure ratings of participants. These findings underscore the presence of complementary competence interaction between user and AI agent within IDSS, and can be applied in designing capability of advisory AI agents with consideration of user proficiency and individual characteristics. Relevance to industryThe findings can be used as guidelines for designing AI agents and specifying their application strategies to integrate the assistance of AI agents effectively in intelligent decision support systems.

Better environments for better AI

AbstractMost AI research focuses exclusively on the AI agent itself, that is, given some input, what are the improvements to the agent's reasoning that will yield the best possible output? In my research, I take a novel approach to increasing the capabilities of AI agents via the use of AI to design the environments in which they are intended to act. My methods identify the inherent capabilities and limitations of AI agents and find the best way to modify their environment in order to maximize performance. With this agenda in mind, I describe here several research projects that vary in their objective, in the AI methodologies that are applied for finding optimal designs, and in the real‐world applications to which they correspond. I also discuss how the different projects fit within my overarching objective of using AI to promote effective multi‐agent collaboration and to enhance the way robots and machines interact with humans.

Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning

AbstractData assimilation (DA) plays a pivotal role in diverse applications, ranging from weather forecasting to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on the Kalman filter's linear update equation to correct each of the ensemble forecast member's state with incoming observations. Recent advancements have witnessed the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a new DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz 63 and 96 systems, where the agent's objective is to maximize the geometric series with terms that are proportional to the negative root‐mean‐squared error (RMSE) between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo‐based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Numerical results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent's capability to assimilate non‐Gaussian observations, addressing one of the limitations of the EnKF.

Digital Literacy Among Agricultural Extension Agents in North Maluku, Indonesia

This study assesses the digital literacy of agricultural extension agents in North Maluku Province, Indonesia, and its implications for service delivery. A survey of 252 extension agents, conducted from November 2022 to February 2023, employed a validated questionnaire based on five key indicators: Information and Communication Technology (ICT) usage, digital data and information literacy, digital communication and collaboration, digital learning and extension skills, and digital identity and security literacy. Descriptive statistical analysis revealed an overall digital literacy level of 54.3% (SD = 10.8), indicating moderate proficiency. While ICT skills proficiency was high (61%), proficiency in other areas was lower, ranging from 50.8% to 55%. Younger agents and those with higher educational levels demonstrated higher digital literacy. These findings underscore the need for targeted interventions by government and academic institutions to bolster the digital capabilities of agricultural extension agents, thereby enhancing their capacity to leverage digital technologies for effective service provision in North Maluku's agricultural sector.

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.

Chiral‐induced highly efficient NIR–photothermal conversion of perylene diimide@silica nanocapsules for photothermal therapy

AbstractPhotothermal agents (PTAs) with ultra‐high photothermal conversion efficiency (PCE) activated upon near‐infrared (NIR) laser irradiation can heat up and destroy tumor cells under low‐intensity laser excitation to allow safe and efficient tumor therapy. Herein, an organic PTA with an outstanding PCE of 89.6% is developed from rationally designed perylene diimide (PDI) with electron‐donating cyclohexylamine moiety at the bay‐positions of its skeleton and chiral phenethylamine (PEA) moiety at its N terminals, termed here PEAPDI. The strong intermolecular interaction between the PDI skeletons induced by PEA together with the intramolecular charge transfer from cyclohexylamine to PDI skeleton severely quenches the fluorescence emission from PEAPDI and significantly enhances its NIR absorption, resulting in super NIR–photothermal conversion. PEAPDI molecules are subsequently encapsulated within silica nanocapsules (SNCs), creating PEAPDI@SNC. Characterized by its small hydrodynamic diameter, monodispersity, high PDI encapsulation efficiency, colloidal stability, and biocompatibility, PEAPDI@SNC exhibits prolonged blood circulation and enhanced permeability and retention effect, enabling targeted accumulation at the tumor site. An in vivo study using a 4T1 tumor–bearing mice model illustrates the agent's potent tumor ablation capability without side effects at low dosage under NIR laser irradiation (808 nm). The findings demonstrate PEAPDI@SNC's significant potential as a PTA for tumor treatment.

Multi-agent cooperative area coverage: A two-stage planning approach based on reinforcement learning

Multi-agent area coverage aims to accomplish the complete traversal of the target area through cooperation between agents. Focusing on the problems of low coverage efficiency and weak practicability in the existing methods, we propose a two-stage area coverage method based on multi-agent deep reinforcement learning. In the first stage, we convert the coverage path planning problem into an optimal grid selection problem, and according to the equivalence of agents in cooperative tasks, we propose a distributed coverage path planning algorithm based on QMIX and a grid coverage map. The second stage is to realize the cooperative navigation control in a constrained environment with obstacles and non-ideal communication conditions. To implement the stage, we design a hybrid attention mechanism to adaptively aggregate important feature information of adjacent agents and obstacles, which efficiently exploits the limited local perception and communication capabilities of agents to perform cooperative control. The experimental results show that the proposed two-stage multi-agent area coverage method can accomplish the area coverage task in the environment with random obstacles, and the area coverage efficiency and robustness are significantly better than other reinforcement learning based or traditional coverage algorithms. In addition, the results also verify that the proposed method has the advantage of adapting to the dynamic changes in the number of agents and the communication range.

Hybrid Nanogel Drug Delivery Systems: Transforming the Tumor Microenvironment through Tumor Tissue Editing.

The future of drug delivery offers immense potential for the creation of nanoplatforms based on nanogels. Nanogels present a significant possibility for pharmaceutical advancements because of their excellent stability and effective drug-loading capability for both hydrophobic and hydrophilic agents. As multifunctional systems, composite nanogels demonstrate the capacity to carry genes, drugs, and diagnostic agents while offering a perfect platform for theranostic multimodal applications. Nanogels can achieve diverse responsiveness and enable the stimuli-responsive release of chemo-/immunotherapy drugs and thus reprogramming cells within the TME in order to inhibit tumor proliferation, progression, and metastasis. In order to achieve active targeting and boost drug accumulation at target sites, particular ligands can be added to nanogels to improve the therapeutic outcomes and enhance the precision of cancer therapy. Modern "immune-specific" nanogels also have extra sophisticated tumor tissue-editing properties. Consequently, the introduction of a multifunctional nanogel-based drug delivery system improves the targeted distribution of immunotherapy drugs and combinational therapeutic treatments, thereby increasing the effectiveness of tumor therapy.

SWOAM: Swarm optimized agents for energy management in grid-interactive connected buildings

With increasing complexity of smart city energy systems and rising energy demand, effective energy management solutions are crucial. Buildings now incorporate renewable energy sources and battery storage for efficient energy utilization, making optimal control strategies important. Compared to rule-based controllers and model-based methods, swarm and evolutionary algorithms have the advantages of providing cost-effective, stable, and scalable alternatives. However, their potential in data-rich environments and multi-building energy systems remains underexplored. This research bridges this gap by demonstrating the cooperative capabilities of population-based optimization agents for efficient energy management. Specifically, novel algorithm and framework, named Swarm Optimized Agents for Sequential Decision Making (SWOAM) is proposed. It combines an online k-means classifier and a swarm optimizer for optimal control of multi-building energy systems. By designing an online K-means learning strategy and an agent-based sequential episodic sampling approach, it is feasible to train a swarm of agents find an optimal energy management policy for buildings in a district. For training and evaluation, we use the standardized CityLearn building energy management environment. The agents are evaluated on three key metrics: electricity cost, carbon dioxide emissions and grid ramping. SWOAM delivers state-of-the-art performance and outperforms modern reinforcement learning and rule-based controllers.

Aggregation-enhanced photothermal therapy of organic dyes.

Photothermal therapy (PTT) represents a groundbreaking approach to targeted disease treatment by harnessing the conversion of light into heat. The efficacy of PTT heavily relies on the capabilities of photothermal agents (PTAs). Among PTAs, those based on organic dyes exhibit notable characteristics such as adjustable light absorption wavelengths, high extinction coefficients, and high compatibility in biological systems. However, a challenge associated with organic dye-based PTAs lies in their efficiency in converting light into heat while maintaining stability. Manipulating dye aggregation is a key aspect in modulating non-radiative decay pathways, aiming to augment heat generation. This review delves into various strategies aimed at improving photothermal performance through constructing aggregation. These strategies including protecting dyes from photodegradation, inhibiting non-photothermal pathways, maintaining space within molecular aggregates, and introducing intermolecular photophysical processes. Overall, this review highlights the precision-driven assembly of organic dyes as a promising frontier in enhancing PTT-related applications. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.