Interaction Of Agents Research Articles

Ethical Challenges for Human–Agent Interaction in Virtual Collaboration at Work

In virtual collaboration at the workplace, a growing number of teams apply supportive conversational agents (CAs). They take on different work-related tasks for teams and single users such as scheduling meetings or stimulating creativity. Previous research merely focused on these positive aspects of introducing CAs at the workplace, omitting ethical challenges faced by teams using these often artificial intelligence (AI)-enabled technologies. Thus, on the one hand, CAs can present themselves as benevolent teammates, but on the other hand, they can collect user data, reduce worker autonomy, or foster social isolation by their service. In this work, we conducted 15 expert interviews with senior researchers from the fields of ethics, collaboration, and computer science in order to derive ethical guidelines for introducing CAs in virtual team collaboration. We derived 14 guidelines and seven research questions to pave the way for future research on the dark sides of human–agent interaction in organizations.

Interaction of the viral infectious agents in the development and exacerbation of the multiple sclerosis

Interaction of the viral infectious agents in the development and exacerbation of the multiple sclerosis

Some anticancer agents as effective glutathione S-transferase (GST) inhibitors

Abstract The objective of this research was to investigate the impact of anthraquinone (AQ) compounds on the activity of the enzyme glutathione S-transferase (GST). The interaction between GST and some AQs (alizarin, purpurin, quinizarin, and dantron) was investigated, and IC50 and K i levels were determined for each compound. The results obtained reveal that these compounds are potent GST inhibitors. K i values of these compounds against GST were found ranging from 9.133 ± 0.895 to 36.992 ± 6.194 μM. In the in vitro study, purpurin was identified as the most potent AQ against GST. Thereafter, binding mode exploration of purpurin to the enzyme was undertaken to elucidate its mechanism of action. To this end, molecular docking was conducted. According to the docking results, purpurin can bind to the enzyme and form a stable complex. Together with this, binding potential of purpurin was less than the standard ligand. Examination of both the inhibitory activity in vitro and molecular docking interactions of these anticancer agents with GST, an enzyme important for detoxification metabolism, led to the identification of important relationships between these compounds and GST. The findings may offer structural direction for developing superior anticancer drugs or powerful GST inhibitors.

Chemotherapy in pediatric brain tumor and the challenge of the blood-brain barrier.

Pediatric brain tumors (PBT) stand as the leading cause of cancer-related deaths in children. Chemoradiation protocols have improved survival rates, even for non-resectable tumors. Nonetheless, radiation therapy carries the risk of numerous adverse effects that can have long-lasting, detrimental effects on the quality of life for survivors. The pursuit of chemotherapeutics that could obviate the need for radiotherapy remains ongoing. Several anti-tumor agents, including sunitinib, valproic acid, carboplatin, and panobinostat, have shown effectiveness in various malignancies but have not proven effective in treating PBT. The presence of the blood-brain barrier (BBB) plays a pivotal role in maintaining suboptimal concentrations of anti-cancer drugs in the central nervous system (CNS). Ongoing research aims to modulate the integrity of the BBB to attain clinically effective drug concentrations in the CNS. However, current findings on the interaction of exogenous chemical agents with the BBB remain limited and do not provide a comprehensive explanation for the ineffectiveness of established anti-cancer drugs in PBT. We conducted our search for chemotherapeutic agents associated with the blood-brain barrier (BBB) using the following keywords: Chemotherapy in Cancer, Chemotherapy in Brain Cancer, Chemotherapy in PBT, BBB Inhibition of Drugs into CNS, Suboptimal Concentration of CNS Drugs, PBT Drugs and BBB, and Potential PBT Drugs. We reviewed each relevant article before compiling the information in our manuscript. For the generation of figures, we utilized BioRender software. We focused our article search on chemical agents for PBT and subsequently investigated the role of the BBB in this context. Our search criteria included clinical trials, both randomized and non-randomized studies, preclinical research, review articles, and research papers. Our research suggests that, despite the availability of potent chemotherapeutic agents for several types of cancer, the effectiveness of these chemical agents in treating PBT has not been comprehensively explored. Additionally, there is a scarcity of studies examining the role of the BBB in the suboptimal outcomes of PBT treatment, despite the effectiveness of these drugs for other types of tumors.

High Capacity Adsorption and Degradation of a Nerve Agent Simulant and a Pesticide by a Nickel Pyrazolate Metal-Organic Framework.

The development of materials that enable the efficient removal of toxic compounds is important for the improvement of current protective materials or decontamination technologies. Current materials rely either on agent removal by adsorption or by effecting (catalytic) degradation. Ideally, both of these mechanisms are combined in a single material in order to target a more broad spectrum of toxic agents and to improve the performance of the materials. Recent attempts to combine materials with either adsorptive or catalytic properties into a composite material are promising, although the overall performance often suffers from competition for the agent between the adsorptive and catalytic domains in the composites. In this work, we propose that metal-organic frameworks (MOFs) could feature both adsorptive properties as well as catalytic properties in a single structural domain, thereby avoiding a reduction in the overall performance originating from competitive agent interactions. We showcase this concept using the MOF Ni3(BTP)2, which exhibits strong affinity and high capacity for the storage of a nerve agent simulant and a pesticide. Moreover, it is demonstrated that the adsorbed agents are efficiently degraded and that the nontoxic degradation products are rapidly expelled from the MOF pores. Its ability to catalyze the hydrolytic degradation of both organophosphate and organophosphorothioate compounds highlights another unique feature of this material. The presented concept illustrates the feasibility for developing materials that target a broader spectrum of agents via adsorption, catalysis, or both and by their broader reactivity toward different types of agents.

Mechanistic and thermodynamic characterization of antiviral inhibitors targeting nucleocapsid N-terminal domain of SARS-CoV-2

The nucleocapsid (N) protein of SARS-CoV-2 plays a pivotal role in encapsulating the viral genome. Developing antiviral treatments for SARS-CoV-2 is imperative due to the diminishing immunity of the available vaccines. This study targets the RNA-binding site located in the N-terminal domain (NTD) of the N-protein to identify the potential antiviral molecules against SARS-CoV-2. A structure-based repurposing approach identified the twelve high-affinity molecules from FDA-approved drugs, natural products, and the LOPAC1280 compound libraries that precisely bind to the RNA binding site within the NTD. The interaction of these potential antiviral agents with the purified NTD protein was thermodynamically characterized using isothermal titration calorimetry (ITC). A fluorescence-based plate assay to assess the RNA binding inhibitory activity of small molecules against the NTD has been employed, and the selected compounds exhibited significant RNA binding inhibition with calculated IC50 values ranging from 8.8 μM to 15.7 μM. Furthermore, the antiviral efficacy of these compounds was evaluated using in vitro cell-based assays targeting the replication of SARS-CoV-2. Remarkably, two compounds, Telmisartan and BMS-189453, displayed potential antiviral activity against SARS-CoV-2, with EC50 values of approximately 1.02 μM and 0.98 μM, and a notable selective index of >98 and > 102, respectively. This study gives valuable insight into developing therapeutic interventions against SARS-CoV-2 by targeting the N-protein, a significant effort given the global public health concern posed due to the virus re-emergence and long COVID-19 disease.

Binary choice with misclassification and social interactions, with an application to peer effects in attitude

The interaction of economic agents is one of the most important elements in economic analyses. Social interactions on subjective outcomes, behavior, or decisions, are inherently difficult to identify and estimate because these variables are prone to misclassification errors. This paper puts forth a binary choice model with misclassification and social interactions to rectify the misclassification problems in social interactions studies. We achieve the identification of the conditional choice probability of the latent dependent variable by the technique of repeated measurements and a monotonicity condition. We construct the complete likelihood function from the two repeated measurements and propose a nested pseudo likelihood algorithm for estimation. Consistency and asymptotic normality results are shown for the proposed estimation method. We illustrate the finite sample performance of the model and the estimation method by three Monte Carlo experiments and an application to the study of peer effects among students in their attitudes towards learning.

Polymerisation of cyanoacrylates: The effect of sclero-embolic and contrast agents.

The objective is to investigate the interaction of sclero-embolic and contrast agents with the polymerisation of medical grade n-butyl-cyanoacrylates. An in vitro spectrophotometric absorbance method was developed to detect changes in light transmission to measure n-BCA polymerisation. The initiation and the rate-of-polymerisation of mixtures of n-BCA with sclero-embolic and contrast agents were investigated. Initiation of polymerisation: VENABLOCK™ and HISTOACRYL® were the fastest agents to polymerise, while VENASEAL™ was the slowest. Rate of polymerisation: Hypertonic saline inhibited the polymerisation of all n-BCAs, while hypertonic glucose prolonged the polymerisation rate. ETHANOL and detergent sclerosants had no effect. Contrast agents OMNIPAQUE™ and ULTRAVIST® initiated and prolonged the polymerisation of n-BCA, but in contrast, LIPIODOL® failed to initiate the process. The commercially available medical cyanoacrylates differ in their polymerisation rates. These polymerisation rates are further affected when these products are used in conjunction with other compounds, such as sclero-embolic and contrast agents.

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

We 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.

Pricing evolution in the Colombian electricity market: Does regulation matter?

Electricity pricing ($/kWh) is a topic of widespread discussion, both for developing and developed countries, because it has begun a worrying upward trend that impacts the well-being of the entire population. Initially, it is expected to reflect the marginal costs of generation and to be affected mainly by the availability of primary resources for electricity generation and the interaction of agents in the market. However, regulations may lead to changes in this expected behavior, for Colombian case, several studies have highlighted the significant impact of the reliability charge (current capacity market in Colombia) on spot price formation, although this capacity market features remuneration independent of the energy market, and these revenues must reflect the costs associated with reliability. The present study conducts an impact evaluation on the time series of the electricity spot price in Colombia to establish whether the resolution that established the reliability charge (CREG071/06) significantly affected the formation of the spot market price. The results suggest that the resolution under study increased the bid of generators in the short and long term, which was accompanied by an average increase of approximately 12% in the spot price of electricity in Colombia.

Development of a multi-loop security system of information interactions in socio-cyberphysical systems

The object of the study is a multi-loop security system of information interactions in socio-cyberphysical systems. The dynamic nature of physical environments inherently challenges the ability of socio-cyber-physical systems to perform adequate control actions for physical assets in many contexts. However, adaptation and evolution actions must be evaluated before implementation in the control system to ensure fault tolerance while minimizing risks. Therefore, the design of socio-cyber-physical systems must ensure not only reliable autonomy, but also operational fault tolerance and safety. The proposed approach is based on the integration of targeted (mixed) threats based on the synthesis of technical cyber threats with social engineering methods. This approach allows forming a dynamic security model based on the analysis of the interaction of various agents in socio-cyberphysical systems, which makes it possible to increase the level of counteraction to targeted (mixed) cyber threats. The results of modeling are based on the proposed classification of threats using social engineering methods, which allows cyberattackers to ensure the probability of implementing targeted threats up to 95–98 %. The proposed classification of threats based on social engineering methods will allow forming an additional parameter for the objectivity of target threats, taking into account their integration and synergy. At the same time, the presented model will make it possible to timely provide knowledge about the possibility of implementing a targeted attack and timely take preventive countermeasures. This approach will improve the set of protection measures, as well as promptly create an increase in the level of resistance of the company's personnel (organization, enterprise, etc.) to threats of social engineering

Lane change trajectory prediction considering driving style uncertainty for autonomous vehicles

Lane change trajectory prediction is crucial for autonomous vehicles (AVs) to assess their own driving safety in advance. However, there are significant uncertainties in the implementation of such prediction, including different behaviors caused by agent–agent interaction and driving styles of drivers. While prototype trajectories can serve as a means to represent typical motion patterns and enhance trajectory prediction performance, their utilization in modeling motion patterns tends to overlook the influence of agent–agent interactions and vehicle dynamics. This paper proposes a fusion algorithm that considers driving style and vehicle dynamics to address these uncertainties. The algorithm involves a long short-term memory (LSTM) lane change behavior recognition model that mines key features of agent–agent interaction through the attention mechanism. The Gaussian process (GP) motion modeling trajectory prediction (GPMM-TP) algorithm considers the driving style of the prototype trajectory based on the behavior recognition results. To further improve short-term and long-term prediction, the interactive multi-model (IMM) algorithm is used to assign probability weights to the GP model and the Extended Kalman Filter (EKF) model based on prediction accuracy, taking into account the driving styles and the vehicle dynamics. The proposed algorithm provides a promising approach to improving the accuracy of lane change trajectory prediction for AVs, and its effectiveness is demonstrated using the HighD dataset.

Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models

Developing effective multi-agent systems (MASs) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of multi-agent deep reinforcement learning (MADRL) in cooperative MASs, one of the major challenges that remain is the simultaneous learning and interaction of independent agents in dynamic environments in the presence of stochastic rewards. State-of-the-art MADRL models struggle to perform well in Coordinated Multi-agent Object Transportation Problems (CMOTPs) wherein agents must coordinate with each other and learn from stochastic rewards. In contrast, humans often learn rapidly to adapt to non-stationary environments that require coordination among people. In this article, motivated by the demonstrated ability of cognitive models based on Instance-based Learning Theory (IBLT) to capture human decisions in many dynamic decision-making tasks, we propose three variants of multi-agent IBL models (MAIBLs). The idea of these MAIBL algorithms is to combine the cognitive mechanisms of IBLT and the techniques of MADRL models to deal with coordination MASs in stochastic environments from the perspective of independent learners. We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of stochastic rewards compared to current MADRL models. We discuss the benefits of integrating cognitive insights into MADRL models.

Experimental study on mix proportion optimization of anti-calcium dissolution shotcrete for tunnels based on response surface methodology

Aiming at the issue of crystallization and blockage of drainage system due to the massive calcium loss from the tunnel shotcrete, a self-designed tunnel seepage crystallization modelling system was developed. This system was produced in conjunction with the initial tunnel support shotcrete construction and drainage pipe installation, and is capable of simulating both the seepage process of groundwater in the shotcrete and the process of crystallization in the drainage pipe. Based on three different mechanisms of anti-crystallization, which include absorbing free calcium, reducing the porosity and increasing hydrophobicity, antialkali agent, nano-calcium carbonate, and silane were selected to test, respectively. Firstly, the suitable dosing ranges of these three external admixtures for resisting calcium loss in shotcrete were determined by single factor tests, which were 7%–11%, 4%–8%, and 0.3%–0.5%, respectively. Thereafter, the response surface method was employed to evaluate the interaction of antialkali agent, nano-calcium carbonate and silane on calcium loss in shotcrete, and to establish the relationship between them, and thus to determine the admixture ratio that can effectively reduce calcium loss crystallization in shotcrete, with the optimal admixture amounts of antialkali agent being 9.242%, nano-calcium carbonate 4.889% and silane 0.366%. Lastly, the reliability of the model test results was verified by the microscopic analysis, and the results showed that the total amount of calcium dissolution in the optimized group could be reduced by 75% compared with the blank control group, and was basically consistent with that derived from the response surface regression model, validating the high accuracy of the buildup response surface regression model. The present study can provide some ideas and references for reducing the seepage crystallization behavior of groundwater in the initial tunnel support shotcrete.

Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments

Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effect is even more significant, as unexpected behavior cause accidents with potentially grave and long-lasting consequences for the involved individuals. In this work, we propose using program synthesis to imitate reinforcement learning policies after seeing a trajectory of the action sequence. Programs have the advantage that they are inherently interpretable and verifiable for correctness. We adapt the state-of-the-art program synthesis system DreamCoder for learning concepts in grid-based environments, specifically, a navigation task and two miniature versions of Atari games, Space Invaders and Asterix. By inspecting the generated libraries, we can make inferences about the concepts the black-box agent has learned and better understand the agent's behavior. We achieve the same by visualizing the agent's decision-making process for the imitated sequences. We evaluate our approach with different types of program synthesizers based on a search-only method, a neural-guided search, and a language model fine-tuned on code.

A Review of Emotions in Human-Conversational Agent Interaction

Conversational agents (CA), powered by natural language processing, have become increasingly popular across multiple domains. However, these agents often fail to communicate effectively with users, leading to poor adoption and task outcomes. Emotions are a fundamental aspect of such interactions, influencing use and adoption of digital artifacts. Despite their salience, understanding of the role of emotions in human-CA collaboration remains fragmented. Motivated thus, we review empirical studies on emotions in human-CA interactions. We synthesize the findings from the reviewed studies in terms of antecedents, emotion-related outcomes, and their relationships in the form of a descriptive model. Based on the synthesis, we identify knowledge gaps and propose directions for future research. Our analysis provides insights into the role of emotions in human-CA interactions and contributes to research in this area.

Soft actor-critic DRL algorithm for interval optimal dispatch of integrated energy systems with uncertainty in demand response and renewable energy

The collaborative optimization dispatching of multiple energy flows plays a crucial role in achieving the economic and efficient low-carbon operation of integrated energy systems (IESs). However, the dispatching problem for IESs is characterized by high dimensionality, non-linearity, and complex coupling. Furthermore, the integration of renewable energy sources and flexible loads has transformed the IES into a complex dynamic system with significant uncertainty. Traditional intelligent optimization algorithms exhibit poor adaptability and lengthy solution computation time when tackling such problems. In contrast, deep reinforcement learning (DRL), as an interactive trial-and-error learning method, has shown improved decision-making capabilities. In view of this, a data-driven soft actor-critic (SAC) deep reinforcement learning-based approach is proposed in this paper for interval optimal dispatch of IESs considering multiple uncertainties. First, the basic principle of SAC reinforcement learning is introduced in detail, and the basic framework of reinforcement learning for interval optimal scheduling of IESs is constructed. Then, the environment model of agent interaction is constructed, and the action and state space of DRL, as well as the reward mechanism and neural network structure, are designed. Finally, a typical IES case is experimentally analyzed and compared with three popular DRL algorithms and five state-of-the-art intelligent optimization algorithms. The experimental results demonstrate the advantages and effectiveness of the proposed method in solving the optimal dispatching of IESs.

Financial corruption; interaction of agents and defective structure

Financial corruption; interaction of agents and defective structure

Discrete Uncertainty Quantification For Offline Reinforcement Learning

Abstract In many Reinforcement Learning (RL) tasks, the classical online interaction of the learning agent with the environment is impractical, either because such interaction is expensive or dangerous. In these cases, previous gathered data can be used, arising what is typically called Offline RL. However, this type of learning faces a large number of challenges, mostly derived from the fact that exploration/exploitation trade-off is overshadowed. In addition, the historical data is usually biased by the way it was obtained, typically, a sub-optimal controller, producing a distributional shift from historical data and the one required to learn the optimal policy. In this paper, we present a novel approach to deal with the uncertainty risen by the absence or sparse presence of some state-action pairs in the learning data. Our approach is based on shaping the reward perceived from the environment to ensure the task is solved. We present the approach and show that combining it with classic online RL methods make them perform as good as state of the art Offline RL algorithms such as CQL and BCQ. Finally, we show that using our method on top of established offline learning algorithms can improve them.

Nitric Oxide as a Determinant of Human Longevity and Health Span.

The master molecular regulators and mechanisms determining longevity and health span include nitric oxide (NO) and superoxide anion radicals (SOR). L-arginine, the NO synthase (NOS) substrate, can restore a healthy ratio between the dangerous SOR and the protective NO radical to promote healthy aging. Antioxidant supplementation orchestrates protection against oxidative stress and damage-L-arginine and antioxidants such as vitamin C increase NO production and bioavailability. Uncoupling of NO generation with the appearance of SOR can be induced by asymmetric dimethylarginine (ADMA). L-arginine can displace ADMA from the site of NO formation if sufficient amounts of the amino acid are available. Antioxidants such as ascorbic acids can scavenge SOR and increase the bioavailability of NO. The topics of this review are the complex interactions of antioxidant agents with L-arginine, which determine NO bioactivity and protection against age-related degeneration.