Artificial Intelligence Agents Research Articles

Large-Language-Model-Based AI Agent for Organic Semiconductor Device Research.

Large language models (LLMs) have attracted widespread attention recently, however, their application in specialized scientific fields still requires deep adaptation. Here, an artificial intelligence (AI) agent for organic field-effect transistors (OFETs) is designed by integrating the generative pre-trained transformer 4 (GPT-4)model with well-trained machine learning (ML) algorithms. It can efficiently extract the experimental parameters of OFETs from scientific literature and reshape them into a structured database, achieving precision and recall rates both exceeding 92%. Combined with well-trained ML models, this AI agent can further provide targeted guidance and suggestions for device design. With prompt engineering and human-in-loop strategies, the agent extracts sufficient information of 709 OFETs from 277 research articles across different publishers and gathers them into a standardized database containing more than 10000 device parameters. Using this database, a ML model based on Extreme Gradient Boosting is trained for device performance judgment. Combined with the interpretation of the high-precision model, the agent has provided a feasible optimization scheme that has tripled the charge transport properties of 2,6-diphenyldithieno[3,2-b:2',3'-d]thiophene OFETs. This work is an effective practice of LLMs in the field of organic optoelectronic devices and expands the research paradigm of organic optoelectronic materials and devices.

Organization philosophy: a study of organizational goodness in the age of human and artificial intelligence collaboration

AbstractThis study challenges the conventional boundaries of philosophy by asserting that organizations can function as legitimate subjects within philosophical discourse. Western philosophy, epitomized by Descartes, has long assumed that individual human beings are the fundamental units of thought and moral agency. However, in a significant oversight, this belief overlooks the idea that organizations can think independently, leading to both virtuous and malevolent results. Epistemology lacks a clear prioritization of morally sound knowledge over potentially harmful knowledge. The advent of artificial intelligence (AI) has also transformed the landscape of cognition from a primarily individual human endeavor into a joint undertaking where humans collaborate with AI entities in collective thinking and decision-making. Nevertheless, AI lacks the innate ability to discern between ethical and unethical actions. This symbiosis creates organizations as fundamental philosophical subjects, marking a significant shift in philosophical emphasis where the organization takes the center stage. Accordingly, within this emerging field, we advance four propositions as the core subjects of organization philosophy: organizations can think, organizations can create knowledge, AI can be part of an organization, and humans in the organization control moral discipline. These propositions guide the ethical coexistence of AI and human agents in organizations, emphasizing the importance of ethical deliberation in knowledge use. Moreover, the malicious use of profound knowledge hinders human progress, as illustrated in this study by an ethical examination of military institutions within organization philosophy.

Channel utilization of media access control protocols for underwater acoustic networks with propagation delay and mobilitya).

This paper investigates the impact of mobility on underwater acoustic communication networks in which the propagation delay is comparable to or larger than the packet duration. An underwater acoustic wireless network, consisting of static and mobile nodes, is studied for its link-layer channel utilization. Synchronous and asynchronous media access control (MAC) protocols are employed with ALOHA, TDMA (time-division multiple access), and artificial intelligence (AI) agent nodes. The simulation results of a multi-node network show that the asynchronous MAC protocols achieve up to 6.66× higher channel utilization than synchronous protocols by allowing time slots to be shorter than the maximum propagation delay among nodes and permitting asynchronous transmission time. The high mobility of a few mobile nodes also favors asynchronous protocols and increases the overall channel utilization. However, node mobility causes more difficulties for the AI node to learn the environment, which may be ineffective to achieve higher gains in channel utilization.

How Being Outvoted by AI Teammates Impacts Human-AI Collaboration

Recent advances in artificial intelligence (AI) enable AI agents to go beyond simply supporting human activities and, instead, take more control in team decision-making. While significant literature has studied human-AI collaboration through the lens of AI as a “second opinion system,” this type of interaction is not fully representative of many human-human team collaboration scenarios, such as scenarios where each decision maker is granted equal voting rights for the team decision. In this research, we explore how imparting AI agents with equal voting rights to the human impacts human-AI decision-making and team performance. Using a human subjects experiment in which participants collaborate with two AI teammates for truss structure (aka, bridge) design, we manipulate a series of voting scenarios (e.g., AI agents outvoting the human vs. AI agents agreeing with the human) and AI performance levels (high vs. low performing). The results indicate that changes in human self-confidence are not consistent with whether the quality of the final team-voted design action is advantageous or disadvantageous relative to their own actions. The results also show that when humans are outvoted by their AI teammates, they do not show strong negative emotional reactions if the team-voted decision has an advantageous outcome. Additionally, AI performance significantly influences the human-AI team decision-making process and even one low-performing AI (i.e., an AI that is frequently incorrect) on the team can significantly deteriorate team performance. Taken together, this research provides empirical evidence on the effects of AI voting with equal decision authority on human-AI collaboration, as well as valuable insights supporting real-world applications of human-AI collaboration via voting.

Can we build a relationship through artificial intelligence (AI)? Understanding the impact of AI on organization-public relationships

The purpose of this study is to explore the role of agent type and voice tone on publics’ perceived organization-public relationships and behavioral intention towards an organization. Through a 2 (agent type: human vs. AI) x 2 (voice tone: conversational human vs. organizational) between-subjects experiment, the study shows that organizational messages communicated by a human agent generated significantly higher perceived control mutuality among participants in a Twitter setting than messages communicated by an AI agent. The use of conversational human voice resulted in significantly stronger relational outcomes. A significant interaction effect was found between agent type and voice tone on control mutuality. This study contributes to the public relations literature and practice by examining the potential role of AI agents in organization-public relationships and supportive behavioral intention towards an organization in the setting of Twitter.

Leveraging Face Recognition Technology for Secure ATM Transaction

ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transactions with ATMs is increasing day by day. ATMs are a very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including fingerprinting, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holders to verify the identity of unauthorized users through some dedicated artificial intelligent agents, for remote certification. However, it is obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone to have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy

Towards Global Explainability of Artificial Intelligence Agent Tactics in Close Air Combat

In this paper, we explore the development of an explainability system for air combat agents trained with reinforcement learning, thus addressing a crucial need in the dynamic and complex realm of air combat. The safety-critical nature of air combat demands not only improved performance but also a deep understanding of artificial intelligence (AI) decision-making processes. Although AI has been applied significantly to air combat, a gap remains in comprehensively explaining an AI agent’s decisions, which is essential for their effective integration and for fostering trust in their actions. Our research involves the creation of an explainability system tailored for agents trained in an air combat environment. Using reinforcement learning, combined with a reward decomposition approach, the system clarifies the agent’s decision making in various tactical situations. This transparency allows for a nuanced understanding of the agent’s behavior, thereby uncovering their strategic preferences and operational patterns. The findings reveal that our system effectively identifies the strengths and weaknesses of an agent’s tactics in different air combat scenarios. This knowledge is essential for debugging and refining the agent’s performance and to ensure that AI agents operate optimally within their intended contexts. The insights gained from our study highlight the crucial role of explainability in improving the integration of AI technologies within air combat systems, thus facilitating more informed tactical decisions and potential advancements in air combat strategies.

Task-driven Risk-bounded Hierarchical Reinforcement Learning Based on Iterative Refinement

Deep Reinforcement Learning (DRL) has garnered substantial acclaim for its versatility and widespread applications across diverse domains. Aligned with human-like learning, DRL is grounded in the fundamental principle of learning from interaction, wherein agents dynamically adjust behavior based on environmental feedback in the form of rewards. This iterative trial-and-error process, mirroring human learning, underscores the importance of observation, experimentation, and feedback in shaping understanding and behavior. DRL agents, trained to navigate complex surroundings, refine their knowledge through hierarchical and abstract representations, empowered by deep neural networks. These representations enable efficient handling of long-horizon tasks and flexible adaptation to novel situations, akin to the human ability to construct mental models for comprehending complex concepts and predicting outcomes. Hence, abstract representation building emerges as a critical aspect in the learning processes of both artificial agents and human learners, particularly in long-horizon tasks. Furthermore, human decision-making, deeply rooted in evolutionary history, exhibits a remarkable capacity to balance the tradeoff between risk and cost across various domains. This cognitive process involves assessing potential negative consequences, evaluating factors such as the likelihood of adverse outcomes, severity of potential harm, and overall uncertainty. Humans intuitively gauge inherent risks and adeptly weigh associated costs, extending beyond monetary expenses to include time, effort, and opportunity costs. The nuanced ability of humans to consider the tradeoff between risk and cost highlights the complexity and adaptability of human decision-making, a skill lacking in typical DRL agents. Principles like these derived from human-like learning present an avenue for inspiring advancements in DRL, fostering the development of more adaptive and intelligent artificial agents. Motivated by these observations and focusing on practical challenges in robotics, our efforts target risk-aware stochastic sequential decision-making problem which is crucial for tasks with extended time frames and varied strategies. A novel integration of model-based conditional planning with DRL is proposed, inspired by hierarchical techniques. This approach breaks down complex tasks into manageable subtasks(motion primitives), ensuring safety constraints and informed decision-making. Unlike existing methods, our approach addresses motion primitive improvement iteratively, employing diverse prioritization functions to guide the search process effectively. This risk-bounded planning algorithm seamlessly integrates conditional planning and motion primitive learning, prioritizing computational efforts for enhanced efficiency within specified time limits.

Reflective Artificial Intelligence

As artificial intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today’s AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward.

How does AI agent (vs. IVR system) service failure impact customer purchase behavior: mediating effect of customer involvement

ABSTRACT In recent years, the use of artificial intelligence (AI) agents in customer service has become a prominent area of study, focusing on their influence over consumer behavior. However, there is a noticeable gap in the current literature regarding AI agent service failures during product information consultations and their effects on customer purchase behavior. The present study, grounded in attribution theory, investigates the effects of various types of self-service failures in both Interactive Voice Response (IVR) systems and AI agents on customer involvement and subsequent purchase behavior. The findings indicate that customers experiencing service failures with AI agents during the consultation stage are more inclined to make purchase behavior after human-led recovery compared to those encountering IVR system failures. Additionally, customer involvement is found to play a pivotal role as a mediator between types of service failures and purchase behavior. The results of this work may offer valuable theoretical and practical insights.

MICIL: Multiple-Instance Class-Incremental Learning for skin cancer whole slide images

Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process. Incremental learning (IL) techniques aim to develop algorithms capable of retaining previously acquired information while also acquiring new insights to predict future data. Deep IL techniques need to address the challenges posed by the gigapixel scale of WSIs, which often necessitates the use of multiple instance learning (MIL) frameworks. In this paper, we introduce an IL algorithm tailored for analyzing WSIs within a MIL paradigm. The proposed Multiple Instance Class-Incremental Learning (MICIL) algorithm combines MIL with class-IL for the first time, allowing for the incremental prediction of multiple skin cancer subtypes from WSIs within a class-IL scenario. Our framework incorporates knowledge distillation and data rehearsal, along with a novel embedding-level distillation, aiming to preserve the latent space at the aggregated WSI level. Results demonstrate the algorithm’s effectiveness in addressing the challenge of balancing IL-specific metrics, such as intransigence and forgetting, and solving the plasticity-stability dilemma.

DeepCraft: imitation learning method in a cointelligent design to production process to deliver architectural scenarios

The recent advancements in digital technologies and artificial intelligence in the architecture, engineering, construction, and operation sector (AECO) have induced high demands on the digital skills of human experts, builders, and workers. At the same time, to satisfy the standards of the production-efficient AECO sector by reducing costs, energy, health risk, material resources, and labor demand through efficient production and construction methods such as design for manufacture and assembly (DfMA), it is necessary to resolve efficiency-related problems in mutual human‒machine collaborations. In this article, a method utilizing artificial intelligence (AI), namely, generative adversarial imitation learning (GAIL), is presented then evaluated in two independent experiments related to the processes of DfMA as an efficient human‒machine collaboration. These experiments include a) training the digital twin of a robot to execute a robotic toolpath according to human gestures and b) the generation of a spatial configuration driven by a human's design intent provided in a demonstration. The framework encompasses human intelligence and creativity, which the AI agent in the learning process observes, understands, learns, and imitates. For both experimental cases, the human demonstration, the agent's training, the toolpath execution, and the assembly configuration process are conducted digitally. Following the scenario generated by an AI agent in a digital space, physical assembly is undertaken by human builders as the next step. The implemented workflow successfully delivers the learned toolpath and scalable spatial assemblies, articulating human intelligence, intuition, and creativity in the cocreative design.

Preserving paradata for accountability of semi-autonomous AI agents in dynamic environments: An archival perspective

This paper proposes the category of real-time artificial intelligence (AI) systems as applications of computerized control systems in dynamic, time-constrained contexts normally managed by human intelligence. Noting the accountability challenges which these systems introduce, the paper posits the need for robust documentation and records capacities within these systems. The paper surveys four real-time AI systems with significant records needs: autonomous vehicles, online content targeting systems, mixed-reality tools for surgical contexts, and digital twin systems in airport facilities management. The paper identifies paradata, or the data leading up to an output in a system's operation, as a key data category necessitating preservation for full transparency in the records generated by these systems. Paradata is defined as “information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.” Paradata uncovers opaque technological processes underlying the production of other datasets and at a granular level must be identified and preserved to delineate the boundaries between human and system agency in semi-autonomous systems. With a basis in control theory, the paper finally offers a framework for assessing the functions of real-time AI systems' operations and their documentation and records needs.

Multi-Objective Reinforcement Learning Application in the View of Agent AI Safety

Reinforcement learning is frequently used to make single decisions using a single decision-based algorithm, where a single objective function is solved, for example, in an agent playing chess, the movement of any player is observed from previous action by considering whether it has received positive or negative reward based on this it will proceed. Nowadays, the requirement of making sequential decisions is complex for this reason. Learning involves using two or more objective functions to address a challenge in multiobjective reinforcement. As an illustration, consider a client purchasing a car and maximising conveniences like minimising fuel use, keeping costs low, and fitting additional luxury items in the automobile. Multi-objective reinforcement learning is applicable in decision-making sequential problem-solving applications. Still, at the same time, we need to avoid the risk that Artificial intelligence agents will be having when problem-solving using MOO-based algorithms. In this paper, we discuss how we use Multi-Objective Optimization (MOO)- based algorithm to low-impact agents for artificial intelligence (AI) safety.

The Effect of AI Agent Gender on Trust and Grounding

Artificial intelligence (AI) agents are widely used in the retail and distribution industry. The primary objective was to investigate whether the gender of AI agents influences trust and grounding. This paper examined the influence of AI agent gender and brand concepts on trust and grounding within virtual brand spaces. For this purpose, it used two independent variables: brand concept (functional vs. experiential) and AI agent gender (male vs. female). The dependent variables included AI agent trust and grounding. The study revealed that in virtual brand spaces centered around a functional concept, male AI agents generated higher levels of trust than female AI agents, whereas, when focused on an experiential concept, female AI agents induced higher levels of grounding than male AI agents. Furthermore, the findings indicate that the association between customers’ identification with AI agents and recommendations for actual brand purchases is mediated by trust and grounding. These findings support the idea that users who strongly identify with AI agents are more inclined to recommend brand products. By presenting alternatives that foster the establishment and sustenance of a meaningful, sustainable relationship between humans and AI, this study contributes to research on human–computer interactions.

A framework of diversity, equity, and inclusion safeguards for chatbots

Chatbots such as ChatGPT are conversational agents that play an important role in many sectors. They are highly sophisticated artificial intelligence (AI) agents increasingly integrated into organizational functions ranging from customer support to recruitment and professional development. Alongside the increasing adoption of chatbots is an increasing focus on diversity, equity, and inclusion (DEI). This article presents a series of chatbot examples and discusses their associated DEI implications, such as those related to AI bias. From the cases reviewed in this article, we extract a framework of DEI safeguards, which includes input safeguards, design safeguards, and functional safeguards. This framework can be used by those involved in developing AI based chatbots or managing their use to ensure that chatbots support, rather than weaken, organizational DEI initiatives and strategies.

Assisting the infection preventionist: Use of artificial intelligence for health care–associated infection surveillance

Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding specialized knowledge and resources. This study investigates the use of artificial intelligence (AI), particularly generative large language models, to improve HAI surveillance. We assessed 2 AI agents, OpenAI's chatGPT plus (GPT-4) and a Mixtral 8×7b-based local model, for their ability to identify Central Line-Associated Bloodstream Infection (CLABSI) and Catheter-Associated Urinary Tract Infection (CAUTI) from 6 National Health Care Safety Network training scenarios. The complexity of these scenarios was analyzed, and responses were matched against expert opinions. Both AI models accurately identified CLABSI and CAUTI in all scenarios when given clear prompts. Challenges appeared with ambiguous prompts including Arabic numeral dates, abbreviations, and special characters, causing occasional inaccuracies in repeated tests. The study demonstrates AI's potential in accurately identifying HAIs like CLABSI and CAUTI. Clear, specific prompts are crucial for reliable AI responses, highlighting the need for human oversight in AI-assisted HAI surveillance. AI shows promise in enhancing HAI surveillance, potentially streamlining tasks, and freeing health care staff for patient-focused activities. Effective AI use requires user education and ongoing AI model refinement.

On the Motivations to Seek Information From Artificial Intelligence Agents Versus Humans: A Risk Information Seeking and Processing Perspective

This study investigates how anticipating an artificial intelligence agent versus human information source moderates the risk information seeking and processing model. It focuses on a behavioral proxy of seeking intention—how long a participant waited for an online consultant whose identity was manipulated. In two samples ( N1 = 182 students and N2 = 800 mturkers), the source identity consistently moderated the model in two ways: First, informational subjective norms encouraged seeking from humans but discouraged seeking from AI agents. Second, information insufficiency drove favoritism toward humans–when perceived information-gathering capacity was high. When the capacity was low, AI agents were favored.

How artificial intelligence cooperating with agent‐based modeling for urban studies: A systematic review

AbstractAs urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent‐based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development.

A stimulus exposure of 50 ms elicits the uncanny valley effect

The uncanny valley (UV) effect captures the observation that artificial entities with near-human appearances tend to create feelings of eeriness. Researchers have proposed many hypotheses to explain the UV effect, but the visual processing mechanisms of the UV have yet to be fully understood. In the present study, we examined if the UV effect is as accessible in brief stimulus exposures compared to long stimulus exposures (Experiment 1). Forty-one participants, aged 21–31, rated each human-robot face presented for either a brief (50 ms) or long duration (3 s) in terms of attractiveness, eeriness, and humanness (UV indices) in a 7-point Likert scale. We found that brief and long exposures to stimuli generated a similar UV effect. This suggests that the UV effect is accessible at early visual processing. We then examined the effect of exposure duration on the categorisation of visual stimuli in Experiment 2. Thirty-three participants, aged 21–31, categorised faces as either human or robot in a two-alternative forced choice task. Their response accuracy and variance were recorded. We found that brief stimulus exposures generated significantly higher response variation and errors than the long exposure condition. This indicated that participants were more uncertain in categorising faces in the brief exposure condition due to insufficient time. Further comparisons between Experiment 1 and 2 revealed that the eeriest faces were not the hardest to categorise. Overall, these findings indicate (1) that both the UV effect and categorical uncertainty can be elicited through brief stimulus exposure, but (2) that categorical uncertainty is unlikely to cause the UV effect. These findings provide insights towards the perception of robotic faces and implications for the design of robots, androids, avatars, and artificial intelligence agents.