Artificial Intelligence Agents Research Articles

Empathic voice assistants: Enhancing consumer responses in voice commerce

Artificial intelligence (AI)-enabled voice assistants (VAs) are transforming firm-customer interactions but often come across as lacking empathy. This challenge may cause business managers to question the overall effectiveness of VAs in shopping contexts. Recognizing empathy as a core design element in the next generation of VAs and the limits of scenario-based studies in voice commerce, this article investigates how empathy exhibited by an existing AI agent (Alexa) may alter consumer shopping responses. AI empathy moderates the original structural model bridging functional, relational, and social-emotional dimensions. Findings of an individual-session online experiment show higher intentions to delegate tasks, seek decision assistance, and trust recommendations from AI agents perceived as empathic. In contrast to individual shoppers, families respond better to functional VA attributes such as ease of use when AI empathy is present. The results contribute to the literature on AI empathy and conversational commerce while informing managerial AI design decisions.

Blockchain: The Economic and Financial Institution for Autonomous AI?

This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating digital economic and financial institutions, permitting AI to engage with these institutions through the management of private keys. This extends AI’s capabilities to form and execute contracts, participate in marketplaces, and utilize financial services autonomously. The paper encourages further research on AI as a general-purpose technology and blockchain as an institutional technology that can unlock the full capabilities of autonomous AI agents.

ALGORITHMIC LITERACY: Generative Artificial Intelligence Technologies for Data Librarians

INTRODUCTION: Artificial intelligence (AI) is a novel type of library technology. AI technologies and the needs of data librarians are hybrid and symbiotic, because academic libraries must insert AI technologies into their information and data services. Library services need AI to interpret the context of big data.OBJECTIVES: In this context, we explore the use of the the OpenAI Codex, a deep learning model trained on Python code from repositories, to generate code scripts for data librarians. This investigation examines the practices, models, and methodologies for obtaining code script insights from complex code environments linked to AI GPT technologies. METHODS: The proposed AI-powered method aims to assist data librarians in creating code scripts using Python libraries and plugins such as the integrated development environment PyCharm, with additional support from the Machinet AI and Bito AI plugins. The process involves collaboration between the data librarian and the AI agent, with the librarian providing a natural language description of the programming problem and the OpenAI Codex generating the solution code in Python.RESULTS: Five specific web-scraping problems are presented. The scripts demonstrate how to extract data, calculate metrics, and write the results to files.CONCLUSION: Overall, this study highlights the application of AI in assisting data librarians with code script creation for web scraping tasks. AI may be a valuable resource for data librarians dealing with big data challenges on the Web. The possibility of creating Python code with AI is of great value, as AI technologies can help data librarians work with various types of data sources. The Python code in Data Science web scraping projects uses a machine-learning model that can generate human-like code to help create and improve the library service for extracting data from a web collection. The ability of nonprogramming data librarians to use AI technologies facilitates their interactions with all types and data sources. The Python programming language has artificial intelligence modules, packages, and plugins such as the OpenAI Codex, which serialises automation and navigation in web browsers to simulate human behaviour on pages by entering passwords, selecting captcha options, collecting data, and creating different collections of datasets to be viewed.

The Rise of Robotic Process Automation in the Banking Sector: Streamlining Operations and Improving Efficiency

Robot Process Automation (RPA) is a type of business process automation that relies on software robots (bots) or artificial intelligence (AI) agents. This phenomenon is sometimes denoted as software robotics, which should not be conflated with robot software. This study investigates the increasing prevalence of RPA across several sectors, with a specific focus on its use in back-office functions. RPA software, exemplified by platforms like Blue Prism, Automation Anywhere, and UiPath, replicates human-computer interactions in order to automate operations that are repetitive and governed by predefined rules. This technology offers many advantages, including cost reduction, mistake minimization, and risk elimination. This research investigates many domains in which RPA may be used, including credible business transformation, content migrations, web crawling/OSINT, and IT department enablement. Additionally, it emphasizes the significant responsibilities within RPA operations, including process architects, technologists, and personnel involved in continuous support and maintenance. The study includes case studies conducted within the banking industry, which demonstrate the potential of RPA in augmenting both customer happiness and productivity. The market report anticipates substantial expansion in the market for RPA software, whereby industry leaders such as UiPath, Automation Anywhere, and Blue Prism are expected to play a dominant role.

Intelligent wireless resource management in industrial camera systems: Reinforcement Learning-based AI-extension for efficient network utilization

In this paper, we present an innovative method for replacing wired communication within an operational agile production cell with wireless communication, using a minimal number of access points. Our approach utilizes Reinforcement Learning (RL) to optimize the layout and minimize the required number of access points. We introduce an Artificial Intelligence (AI) agent that facilitates cooperative interaction between cameras and access points, optimizing camera stream utilization and radio coverage. This solution benefits from the recent standardization of The fifth generation of mobile communications (5G) Network Resource Management (NRM) features of the User Equipment (UE) attached to the cameras. To accommodate the dynamic nature of the production cell, our optimization algorithms run during cell operation, necessitating extended training times.Our contributions extend beyond the state-of-the-art by offering a Digital Twin (DT) environment equipped with physics and radio propagation capabilities, fast raytracing for radio propagation modeling, curriculum-learning-based reinforcement learning, and a Physical Twin (PT) of the radio-connected camera with self-adjusting scene setup capabilities. This comprehensive proposal combines the efficiency of 5G radio utilization with precise modeling and high-resolution simulations, while also significantly improving training speed.In the Agile Robotics for Industrial Automation Competition (ARIAC) 2020 environment, our methods showed compelling results, with curriculum learning achieving a remarkable 3x speed-up compared to non-curriculum approaches. We also demonstrated that high Quality of Service (QoS) demands can be reduced to just 2% of total time while maintaining consistent productivity Key Performance Indicators (KPIs). Furthermore, we explored the integration of these methods into Internet of Things (IoT) devices with limited performance capabilities. In conclusion, our approach offers significant benefits and savings for IoT devices, networks, edge clouds, and industry players.

CAT Bridge: an efficient toolkit for gene-metabolite association mining from multiomics data.

With advancements in sequencing and mass spectrometry technologies, multiomics data can now be easily acquired for understanding complex biological systems. Nevertheless, substantial challenges remain in determining the association between gene-metabolite pairs due to the nonlinear and multifactorial interactions within cellular networks. The complexity arises from the interplay of multiple genes and metabolites, often involving feedback loops and time-dependent regulatory mechanisms that are not easily captured by traditional analysis methods. Here, we introduce Compounds And Transcripts Bridge (abbreviated as CAT Bridge, available at https://catbridge.work), a free user-friendly platform for longitudinal multiomics analysis to efficiently identify transcripts associated with metabolites using time-series omics data. To evaluate the association of gene-metabolite pairs, CAT Bridge is a pioneering work benchmarking a set of statistical methods spanning causality estimation and correlation coefficient calculation for multiomics analysis. Additionally, CAT Bridge features an artificial intelligence agent to assist users interpreting the association results. We applied CAT Bridge to experimentally obtained Capsicum chinense (chili pepper) and public human and Escherichia coli time-series transcriptome and metabolome datasets. CAT Bridge successfully identified genes involved in the biosynthesis of capsaicin in C. chinense. Furthermore, case study results showed that the convergent cross-mapping method outperforms traditional approaches in longitudinal multiomics analyses. CAT Bridge simplifies access to various established methods for longitudinal multiomics analysis and enables researchers to swiftly identify associated gene-metabolite pairs for further validation.

Functional structure of reflexive-active systems of artificial heterogeneous intelligent agents

The paper is aimed at developing distributed artificial intelligence systems for relevant simulation of collective solving of practical problems by modeling reflection. The paper presents the basic design principles and typical functional structure of reflexive-active systems of artificial heterogeneous intelligent agents. Agents of such systems mutually model each other’s reflexive positions; dynamically develop tactics and strategies for their behavior that are relevant to the models of their counterparties; if it is necessary during problem solving, attract new agents from the pool of available agents built by various developers and exclude existing ones, rebuilding “on the fly” the composition of agents and the structure of connections between them.

Forecasting Future Behavior: Agents in Board Game Strategy

This paper presents findings on machine learning agent behavior prediction in a board game application developed by a group of students. The goal of this research is to create a model facilitating collaboration between a user and an AI to play together in the board game using a Human-in-the-Loop architecture. By injecting explainability, the aim is to enhance communication and understanding between the user and the AI agent. Featuring a competitive Artificial Intelligence (AI) based on the Proximal Policy Optimization model, this research explores methods to make AI decisions transparent for enhanced player understanding. Two predictive models, a Decision Tree (DT) and a Deep Learning (DL) classifier, were developed and compared. The results show that the DT model is effective for short-term predictions but limited in broader applications, while the DL classifier shows potential for long-term prediction without requiring direct access to the game's AI. This study contributes to understanding human-AI interaction in gaming and offers insights into AI decision-making processes.

Human divergent exploration capacity for material design: A comparison with artificial intelligence

Applications of artificial intelligence (AI) to material design have attracted increasing attention in recent years. Although AI-aided material design holds great promise for some applications, whether it has surpassed human creativity remains uncertain. The aim of the current study was to compare the divergent exploration capacity of AI with that of humans on a material design task. Human participants were asked to find a high-performance lubricant molecule under searching conditions comparable to a state-of-the-art AI system. Results indicated that, on average, AI was able to find significantly better lubricant molecules. However, the best molecule found by AI fell short of the best molecule found by a human participant. Furthermore, the structural characteristics of the molecules found by AI and human participants differed significantly. These findings suggest that a state-of-the-art AI system is capable of surpassing human divergent exploration capacity in material design, as in other fields in which AI has advanced. Nevertheless, our results also demonstrate that human intelligence and AI can play complementary roles in covering a broader search space. This investigation opens up new possibilities for collaborative systems involving both AI agents and humans in material design.

Towards a science exocortex

We propose a science exocortex—a swarm of artificial intelligence (AI) agents coordinating with each other to accomplish tasks, thereby expanding a researcher's cognitive abilities.

Evolução do uso de agentes conversacionais na educação empresarial: Passado, presente e futuro

Abstract Purpose: The primary objective of this article is to answer the proposed research questions by analyzing the publications on artificial intelligence (AI), education, business, and conversational agents (CAs) or chatbots within the Scopus database to identify the most frequent terms used by scholars over the years, as well as to classify the research evolution based on the most important thematic trends. Originality/value: The study contributes to the literature by offering insights into the thematic landscape of AI-related research across multiple domains. By employing a comprehensive methodology, the article provides a nuanced understanding of the intersection between AI, education, business, and CAs. Design/methodology/approach: The methodology comprises two main components. Firstly, topic modeling is applied by the year of publication to summaries of articles meeting the search criteria. Secondly, grounded theory coding was used to categorize generated themes into more meaningful classifications. This dual approach ensures a rigorous data analysis and facilitates the identification of overarching thematic trends. Findings: The results reveal five thematic trends investigated in the analyzed publications: 1. student-centered learning in higher education; 2. interactive methods using the natural language processing (NLP) approach; 3. technological solutions and ChatGPT in a university context; 4. enhancing education through intelligent platforms; and 5. challenges for social and academic integration of AI tools. Additionally, the study proposes a research agenda with some questions as future avenues of inquiry.

Advice utilization in combined human-algorithm decision making: An analysis of preferences and behaviors

As artificial intelligence (AI) becomes more pervasive, humans will interact with autonomous agents more frequently and in deeper ways. While there is a significant body of work addressing the interface between a single human and a single AI agent, less is known about how individuals react to AI when they are part of human-agent hybrids, namely multiple humans and potentially multiple AI. These hybrid forms are unique in that advice is often given simultaneously, i.e., a human decision maker evaluates advice from other humans and algorithms at the same time. This scenario presents a boundary condition on the extant literature, as it is unclear how a human decision maker will differentially appraise a human advisor compared to an algorithmic advisor when their advice is simultaneous. This study presents the results of three experiments asking individuals to estimate property rental prices with the support of both human and algorithmic advice. We tested whether explicitly labeling an advisor as an algorithm rather than a human impacts how individuals perceive both the algorithm and another human advisor. We also examined the role of conflicting advice during simultaneous evaluation. Based on the results of 904 participants, we found that labeling an advisor as an algorithm resulted in a significantly significant algorithmic appreciation bias, even when an equivalent human was present. Further, we found that uncertainty induced by conflicting information weakened the appreciation effect, while agreement among advisors resulted in the strongest behavioral responses.

Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has been developed and experiments have been carried out to create phenotypes of intelligent agents based on them. A complex genome of an intelligent agent has been developed, the features of a multichromosome genetic algorithm for organizing calculations in the paradigm of multigenerational optimization of multiagent neurocognitive architectures have been established and substantiated. It is shown that multigenerational optimization of the multi-agent neurocognitive architecture of intelligent agents can contribute to the achievement of adaptive resistance to the operating conditions of a general artificial intelligence agent, provide the synthesis of its suboptimal structural and functional scheme, accelerate learning and algorithms for finding solutions to a universal range of problems solved by this agent in its ecological niche.

The Philosophical and Legal Rationale for a Systematic Analysis of Digital Dispute Resolution Models in Modern Arbitration

In the article, the author presents a systematic analysis of models of digital dispute resolution in modern arbitration. The author studied the dispute resolution models on the platforms “Kleros”, “Aragon”, “CodeLegit”, as well as the Draft arbitration rules for smart contracts “JAMS-2018” and the English “DDRR-2021”. The author identifies the following types of models of arbitration dispute resolution: 1) traditional arbitration; 2) traditional arbitration with blockchain elements (a model based on the CodeLegit platform), 3) digital arbitration (“DDRR-2021”). The most important feature and difference of the English “Digital DR Regulation” 2021 is the fact that the entire process from the beginning (occurrence of the case) to the end (execution of the decision) is resolved automatically without the intervention of human arbitrators with the help of an artificial intelligence agent. This is the procedure for resolving a dispute in the field of smart contracts that should be called digital arbitration. The so-called “decentralized arbitration” on the platforms “Kleros”, “Aragon”, “OpenLaw”, “Mattereum Protocol”, “Rhubarb Fund”, “Jury.Online”, “Jur”, “OATH Protocol”, “Juris” and other models of this type does not allow these models to be considered arbitration. The author believes that these models should be conditionally called crowdsourcing quasi-arbitration.

Digital Arbitration Is a New Way of Dispute Resolution for the Unified Digital Space of the EAEU: Political, Philosophical and Legal Aspect

The article discusses the theoretical legal foundations of a new type of arbitration – digital arbitration (or blockchain arbitration). The author formulated the concept of digital arbitration and analyzed the differences between digital arbitration and traditional arbitration from the point of view of theories about the legal nature of arbitration. In particular, the author believes that the term digital arbitration (blockchain arbitration) is used in three meanings. Firstly, the term digital arbitration refers to a way to protect the rights arising from smart contracts. This method is considered as an alternative to those methods that imply the need to seek judicial protection from the State or traditional arbitration. Secondly, digital arbitration refers to the body that organizes the digital trial of a legal dispute. And, thirdly, this concept denotes an artificial intelligence agent (robot), which considered the dispute submitted for its resolution. The author believes that due to its features, digital arbitration can be recommended as an alternative way to resolve disputes in the digital space of the EAEU.

AI аnd Machine Translation Post-editing: Advancements and Challenges (Insights for Students of International Studies)

Now, with the appearance of the latest wave of a sophisticated generative artificial intelligence (AI), humanity is about to embark on an entirely new functioning order. The challenge today is that due to AI the world will definitely undergo drastic metamorphosis tomorrow, and again the day after. To adapt to this reinvented economy, people will need to reinvent their skills, careers – and, indeed, their lives. Therefore, educating people for reinvention in this fluid context will require the reinvention of higher education itself. According to Joseph E. Aoun, taking into consideration the targets to be met in the nearest future, the next generation of HEI students are supposed to be educated to invent, to create, and to discover – to meet society’s targets that any most sophisticated artificial intelligence agent cannot, consequently, a curriculum should include technological literacy, or understanding how machines work and how to work with them. Furthermore, the emergence of cognitive translation studies has stipulated an interdisciplinary approach to delve into the cognitive and behavioural aspects of a broad array of cross-language activities including all kinds of translation and interpreting. In a world that relentlessly pursues efficiency and productivity, the figure of a post-editor, a professional translator who has the skills to add that necessary human touch to a text which has previously been subjected to software algorithms, has become more prominent.

A novel reinforcement learning method for the plan of generator start-up after blackout

With the expansion of the power system and the impact of natural disasters, blackouts are almost inevitable. A scientific restoration plan can effectively shorten the economic and social losses caused by blackouts. As the first step of power system restoration, the generator start-up process impacts the success rate and time consumption. Considering the diversity of constraints in the generator start-up process and the complexity of the topology, this paper proposes a planning method for the generator start-up process based on reinforcement learning. Q-learning is a model-free reinforcement learning algorithm that can cope well with complex environments and diverse constraints. First, the generator start-up problem is modeled as a Markov Decision Process, and the restoration operation and constraints are considered. Q-learning uses an artificial intelligence agent to plan the generator start-up process and guide operators. Second, different from the restoration methods of sequential restarting the generators, this paper presents a novel strategy for restoration by restoring the path of the subsequent generator while the current generator acquires cranking power. Then, the action selection mechanism is optimized by using the Upper Confidence Bound algorithm to address the shortcoming of low exploration effectiveness in the conventional Q-learning. Finally, the simulation results provide evidence of the excellent performance of the proposed method in terms of optimality and convergence.

Artificial Intelligence Agents for Materials Sciences.

The artificial intelligence (AI) tools based on large-language models may serve as a demonstration that we are reaching a groundbreaking new paradigm in which machines themselves will generate knowledge autonomously. This statement is based on the assumption that the ability to master natural languages is the ultimate frontier for this new paradigm and perhaps an essential step to achieving the so-called general artificial intelligence. Autonomous knowledge generation implies that a machine will be able, for instance, to retrieve and understand the contents of the scientific literature and provide interpretations for existing data, allowing it to propose and address new scientific problems. While one may assume that the continued development of AI tools exploiting large-language models, with more data used for training, may lead these systems to learn autonomously, this learning can be accelerated by devising human-assisted strategies to deal with specific tasks. For example, strategies may be implemented for AI tools to emulate the analysis of multivariate data by human experts or in identifying and explaining patterns in temporal series. In addition to generic AI tools, such as Chat AIs, one may conceive personal AI agents, potentially working together, that are likely to serve end users in the near future. In this perspective paper, we discuss the development of this type of agent, focusing on its architecture and requirements. As a proof-of-concept, we exemplify how such an AI agent could work to assist researchers in materials sciences.

Empathic accuracy in artificial intelligence service recovery

PropósitoLas agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.Diseño/Metodología/EnfoqueSe presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.HallazgosLos resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.OriginalidadEste artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.

Scientometric analysis on the use of ChatGPT, artificial intelligence, or intelligent conversational agent in the role of medical training

IntroductionIn recent times, there has been a noticeable surge in the usage of artificial intelligence, including ChatGPT and other types, in the field of health sciences education. In this regard, an exploratory bibliometric study was carried out to examine the utilization of smart conversational agents, ChatGPT, and artificial intelligence bots in medical education. MethodsA retrospective, observational, cross-sectional bibliometric analysis was employed to assess the scientific publications listed in Scopus. This study was conducted on March 11, 2023 in search for information in Scopus. A total of 220 relevant documents were identified that were available in the Scopus database during the period between 2017 and 2022. Elsevier's SciVal software was used. Subsequently, statistical tables and graphs were prepared for presentation in Bibliometrix software. ResultsAmong the authors, Timothy W. Bickmore, from the United States, has the highest number of publications (10) and citations received (172), and an h-index of 45, suggesting a significant influence in the field of study. The subcategory with the highest academic output is Health Informatics with 133 publications, while Geriatrics and Gerontology has the least with only 3. Most of the analyzed publications (44.2%) originated from collaborations within the same country. Notably, the Swiss Federal Institute of Technology Zurich and Imperial College London stood out with 12 publications each that received over 200 citations indicating their significant impact on their respective fields. Despite having the highest number of academic publications (15), Brazil had a relatively low field-weighted citation impact (0.64) and received the lowest number of citations (81). A clustering analysis was performed on a sample of 10 concepts using 2 dimensions. The results indicated that all terms were part of the same cluster. Notably, the terms 'conversational agents', 'chatbots', 'conversational agent', and 'chatbot' were closely related. ConclusionsIt was found that the American Bickmore, Timothy W., led the top-10 researchers, and that the Health Informatics subject area was the most predominant. However, Brazil and Germany were the leading countries in terms of research output that was mainly published in high impact journals (Q1).