Software Agents Research Articles

The group mind of hybrid teams with humans and intelligent agents in knowledge-intense work

Studies regularly demonstrate how well intelligent agents (IAs) can support humans or are demonstrably superior to them in some areas. Given that some tasks likely remain unsuitable for even the most intelligent machines in the mid-future, work in hybrid teams of humans and IAs—where the capabilities of both are effectively combined—will most likely shape the way we work in the coming decades. In an abductive study, we investigate an early example of hybrid teams, consisting of a conversational intelligent agent (IA) and humans, that aims to improve health behavior or change personality traits. We theorize Transactive Intelligent Memory System (TIMS) as a new vision of collaboration between humans and IAs in hybrid teams, based on our empirical insights and our literature review on transactive memory systems theory. Our empirical evidence shows that IAs can develop a form of individual and external memory, and hybrid teams of humans and IAs can realize joint systems of transactive memory—a competence that current literature only ascribes to humans. We further find that whether individuals view IAs merely as external memory aids or as part of their teams’ transactive memory is moderated by the tasks’ complexity and knowledge intensity, as well as the IA's ability to complete the task. This theorizing helps to better understand the role of IAs in future team-based working processes. Developers of IAs can use TIMS as a tool for requirements formulation to prepare their software agents for collaboration in hybrid teams.

AGENT Q-LEARNING SPEED DEPENDENCY ON THE FACTORS IN TIC-TAC-TOE

Reinforcement learning of program agents has widespread usage today. Especially, Q-learning, a model-free reinforcement learning technique has shown great results in various applications, like games, self-driving car and robot control. Regarding turn-based games many scientists successfully applied it to train artificial intelligence and to make competitive opponent for human player. While algorithms are well-known there is a room for parametrical optimization to achieve maximum learning speed considering specific problem like turn-based board game. As results of this research show the speed can vary significantly. Tic-Tac-Toe is a very simple and old game that gives an opportunity to try Q-learning without excessive efforts. The algorithm is universal and can be applied to more complex games. It worth noting that core of learning algorithm is the same for any similar game – only rules and board size are changed, which is one of the important properties of Q-learning. This paper investigates the impact of learning rate and discount factor on the speed of learning of the Q-learning program agent in Tic-Tac-Toe board game. It is conducted a series of experiments using developed computer implementation of the algorithm to analyze the correlation between learning rate, discount factor, and the convergence rate of Q-learning in the specified game. So the experimental field consists of two factors, each of which has three levels, and full factorial experiment has nine combinations of these factors. The learning speed dependency on the each factor is presented. The findings reveal strong relationships between these parameters and convergence speed. For example, the speed is increased proportionally to the both factors, but in the case of discount factor the increase is about 1.4 times lower. Practical significance of the research is in the optimization of factors to achieve effective training of the software agent in order to save processing time, the payment of which is one of the main expenses of enterprises in the field of information technologies. In addition, the research contributes to a better understanding of how Q-learning performs in different game scenarios and provides guidelines for parameter selection in similar applications

Offering services and self-service in intelligent public administration

Self-service and offering services in public administration is a new concept that should be the basis of its service in the future. Here, we present a concept that should enable not only more efficient service but also the satisfaction of service users. Especially because it would provide them with the option to choose. To implement this concept effectively, it is necessary to implement intelligent public administration that can provide it. For this, we need knowledge bases and intelligent software agents. We will list some of the tools for implementing knowledge bases, and in one tool, we will create an appropriate database. We will use it to present the process of offering services. Here, we will mention the process of forwarding the "Birth Certificate" - a birth certificate that is part of self-service. We will mention offering a "Criminal Record Certificate" and "Title deed" as an example of offering services. These are a unique public administration data warehouse and a necessary means for user identification. There are also necessary legal prerequisites for implementing and using self-service and offering services in intelligent public administration.

The Most Disruptive Near-Term Use of AI in Cancer Care: Patient Empowerment Through Software Agents

The Most Disruptive Near-Term Use of AI in Cancer Care: Patient Empowerment Through Software Agents

Multi-agent platform to support trading decisions in the FOREX market

Trading decisions often encounter risk and uncertainty complexities, significantly influencing their overall performance. Recognizing the intricacies of this challenge, computational models within information systems have become essential to support and augment trading decisions. The paper introduces the concepts of trading software agents, investment strategies, and evaluation functions that automate the selection of the most suitable strategy in near real-time, offering the potential to enhance trading effectiveness. This approach holds the promise of significantly increasing the effectiveness of investments. The research also seeks to discern how changing market conditions influence the performance of these strategies, emphasizing that no single agent or strategy universally outperforms the rest. In summary, the overarching objective of this research is to contribute to the realm of financial decision-making by introducing a pragmatic platform and strategies tailored for traders, investors, and market participants in the FOREX market. Ultimately, this endeavor aims to empower people with more informed and productive trading decisions. The contributions of this work extend beyond the theoretical realm, demonstrating a commitment to address the practical challenges faced by traders and investors in real-time decision-making within the financial markets. This multidimensional approach to financial decision support promises to enhance investment effectiveness and contribute to the broader field of algorithmic trading.

Measuring User Response to Attention Guidance Using the Integrated Cognitive User Assistance System

Humans are limited in their attentional capacity and miss salient cues when multitasking. We built a platform, ICUa (Integrated Cognitive User assistance) that simulates parts of MATBII, a computer-based multitasking testbed, adding gaze-based attention guidance in an attempt to improve multitasking performance. ICUa uses software agents for monitoring the interface, human behavior and eye gaze in order to guide attention (displaying highlights and arrows). We varied the difficulty of our testbed and found effects on human performance (based on error times) and characterized the types of errors that occurred with higher difficulty and how agent response changed. There was no clear effect of ICUa guidance on gaze patterns or human performance. The visual guidance we deployed may not be sufficient to measurably influence behavior. Future work will develop the detection eye gaze patterns and effective cues to redirect attention using this human-agent interface for experiments on attention guidance.

Physician Adoption of AI Assistant

Problem definition: Artificial intelligence (AI) assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. In this paper, we investigate the impact of the AI smartness (i.e., whether the AI assistant is powered by machine learning intelligence) and the impact of AI transparency (i.e., whether physicians are informed of the AI assistant). Methodology/results: We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, that is, adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that the smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing. Moreover, the impact of AI transparency on the adoption rate is contingent on the smartness level of the AI assistant: the transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the AI assistant is smart. Managerial implications: Our study can guide platforms in designing their AI strategies. Platforms should improve the smartness of AI assistants. If such an improvement is too costly, the platform should transparentize the AI assistant, especially when it is not smart. Funding: This research was supported by a Behavioral Research Assistance Grant from the C. T. Bauer College of Business, University of Houston. H. Zhao acknowledges support from Hong Kong General Research Fund [9043593]. Y. (R.) Tan acknowledges generous support from CEIBS Research [Grant AG24QCS]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0093 .

Efficacy of relational agents for loneliness across age groups: a systematic review and meta-analysis

BackgroundLoneliness is a serious public health concern. Although previous interventions have had some success in mitigating loneliness, the field is in search of novel, more effective, and more scalable solutions. Here, we focus on “relational agents”, a form of software agents that are increasingly powered by artificial intelligence and large language models (LLMs). We report on a systematic review and meta-analysis to investigate the impact of relational agents on loneliness across age groups.MethodsIn this systematic review and meta-analysis, we searched 11 databases including Ovid MEDLINE and Embase from inception to Sep 16, 2022. We included randomised controlled trials and non-randomised studies of interventions published in English across all age groups. These loneliness interventions, typically attempt to improve social skills, social support, social interaction, and maladaptive cognitions. Peer-reviewed journal articles, books, book chapters, Master’s and PhD theses, or conference papers were eligible for inclusion. Two reviewers independently screened studies, extracted data, and assessed risk of bias via the RoB 2 and ROBINS-I tools. We calculated pooled estimates of Hedge’s g in a random-effects meta-analysis and conducted sensitivity and sub-group analyses. We evaluated publication bias via funnel plots, Egger’s test, and a trim-and-fill algorithm.FindingsOur search identified 3,935 records of which 14 met eligibility criteria and were included in our meta-analysis. Included studies comprised 286 participants with individual study sample sizes ranging from 4 to 42 participants (x̄ = 20.43, s = 11.58, x̃ = 20). We used a Bonferroni correction with αBonferroni = 0.05 / 4 = 0.0125 and applied Knapp-Hartung adjustments. Relational agents reduced loneliness significantly at an adjusted αBonferroni (g = -0.552; 95% Knapp-Hartung CI, -0.877 to -0.226; P = 0.003), which corresponds to a moderate reduction in loneliness.ConclusionOur results are currently the most comprehensive of their kind and provide promising evidence for the efficacy of relational agents. Relational agents are a promising technology that can alleviate loneliness in a scalable way and that can be a meaningful complement to other approaches. The advent of LLMs should boost their efficacy, and further research is needed to explore the optimal design and use of relational agents. Future research could also address shortcomings of current results, such as small sample sizes and high risk of bias. Particularly young audiences have been overlooked in past research.

Онтолого-орiєнтована мультиагентна система для децентралiзованого керування групою БПЛА

Introduction. Today, UAVs are becoming an increasingly important tool for performing complex tasks in various fields of application, both civil (economic) and military, as they are particularly effective in dynamically uncertain environments with hard-to-reach areas. In addition, technological advances such as blockchain, artificial intelligence (AI) and machine learning have enabled the development of updated and improved UAV systems. To create and deploy a swarm of UAVs, coordinate actions, manage, and exchange data, a model of a multi-agent system (MAC) based on an ontological representation of knowledge is proposed. This model enables a swarm of UAVs to effectively make decisions in various situations while performing assigned tasks. This approach enables the safety, reliability, and efficiency of the tasks of the UAV group. The purpose of the paper is to develop further the theoretical and practical foundations of the integration of the multi-agent system (MAS) based on the ontological representation of knowledge with the UAV network. This involves the development of a MAS architecture and a hierarchical set of ontologies of different levels. The goal is to create a common data description lan guage, define data semantics to ensure data uniqueness and consistency, provide support for decision-making during UAV swarm management, and swarm survivability in the event of aircraft failures or loss. It is necessary to develop algorithms and a method of dividing a complex task into sub-tasks in a swarm of UAVs among all MAS agents. This is to ensure reliable exchange of messages (data) between agents during the joint performance of the assigned task, and the possibility of dynamic redistribution of roles between UAV agents as needed. Methods. During the research, the general theory of intelligent information technologies was applied; agent theory methods in particular intelligent BDI agents; methods of analyzing the performance of wireless data exchange networks; theory of combinatorial optimization for dividing tasks into subtasks; methods of ontological analysis and descriptive logic to create an ontological hierarchical model of the subject area; methods of enriching ontological models from external semantically marked information resources. Results. As a result of the performed scientific research, the MAS architecture was proposed and its main functions were determined for the decentralized control of a swarm of UAVs. A set of agents with assigned roles was formed, who jointly (cooperatively) perform tasks, exchanging messages, and information with each other, which ensures the survivability of the system (in case of a failure or loss of the device, its task must be distributed among other drones). Plans and scenarios of MAS actions for various situations and means of coordinating actions between agents have been developed to perform the mission by a swarm of UAVs. A hierarchical ontological model of the subject area related to the work of the UAV swarm has been created. The algorithms and methods were based on the integration of semantic technologies that support the MAS during the execution of the UAV swarm mission, decision-making, assessment of the dynamic environment, and response to its changes. Conclusions. An original approach, algorithms, and method for improving the system of decentralized control of a group of UAVs were proposed. Expanding the functionality of the system for maintaining the interaction of a swarm of unmanned systems based on MAS artificial intelligence was suggested. This system was based on ontological models. The models describe knowledge of the subject area, processes of UAV swarm operation, scenarios of actions in difficult situations, distribution of roles to agents, principles of planning, and coordination. The proposed MAS is integrated with the UAV swarm software platform, which makes it possible to improve the efficiency of the decentralized control system and adapt UAVs to dynamic changes in the environment. The practical result of the work will be a prototype of a software agent system that interacts with ontologies while performing simple tasks. The economic significance of the work consists of focusing on the creation of new intelligent information technologies, which were based on AI and knowledge of the subject area, and this significantly increases the efficiency of the functioning of modern systems. Keywords: multi-agent system, ontology, formalization of knowledge, UAV, drone, decentralized control, task allocation.

Providing healthcare shopping advice through knowledge-based virtual agents

Knowledge-based virtual shopping agents, that advise their users about which products to buy, are well used in technical markets such as healthcare e-commerce. To ensure the proper adoption of this technology, it is important to consider aspects of users’ psychology early in the software design process. When traditional adoption models such as UTAUT-2 work well for many technologies, they overlook important specificities of the healthcare e-commerce domain and of knowledge-based virtual agents technology. Drawing upon health information technology and virtual agent literature, we propose a complementary adoption model incorporating new predictors and moderators reflecting these domains’ specificities. The model is tested using 903 observations gathered through an online survey conducted in collaboration with a major actor in the herbal medicine market. Our model can serve as a basis for many phases of the knowledge-based agents software development. We propose actionable recommendations for practitioners and ideas for further research.

Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction

Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted, adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data are securely stored on a blockchain using the LWEA encryption method.

Web3-DAO: An ontology for decentralized autonomous organizations

Decentralized autonomous organizations (DAOs) are relatively a newly emerging type of online entity related to governance or business models where all their members work together and participate in the decision-making processes affecting the DAO in a decentralized, collective, fair, and democratic manner. In a DAO, members interaction is mediated by software agents running on a blockchain that encode the governance of the specific entity in terms of rules that optimize their business and goals. In this context, most popular DAO software frameworks provide decision-making models aiming to facilitate digital governance and the collaboration among their members intertwining social and economic concerns. However, these models are complex, not interoperable among them and lack a common understanding and shared knowledge concerning DAOs, as well as the computational semantics needed to enable automated validation, simulation or execution. Thus, this paper presents an ontology (Web3-DAO), which can support machine-readable digital governance of DAOs adding semantics to their decision-making models. The proposed ontology captures the domain logic that allows the sharing of updated information and decisions for all the members that interact with a DAO by the interoperability of their own assessment and decision tools. Furthermore, the ontology detects semantic ambiguities, uncertainties and contradictions. The Web3-DAO ontology is available in open access at https://github.com/Grasia/semantic-web3-dao.

Robotics by multimodal self-organizing ensembles of software and hardware agents with artificial intelligence

Self-organizing ensembles of software and hardware agents with artificial intelligence model the intellectual abilities of a person's natural intelligence. The Creator endowed man with various types of intellectual abilities: generation of meanings, perception of meanings, meaningful actions and behavior, sensory reaction to meanings, emotional reaction to meanings. Based on the synergy of various intellectual abilities, a person carries out life activities. For example, Dialogue is conducted on the basis of two intellectual abilities: the generation and perception of meanings. A multimodal self-organizing ensemble of intelligent software and hardware agents with artificial intelligence, based on existing knowledge and skills, is able to write poetry, draw pictures, give recommendations and solutions to specialists, manage production and systems in various sectors of the economy, and take part in scientific research. Multimodal ensembles of intelligent agents, modeling the functions of natural intelligence, contain a functional control structure. To ensure the safe and reliable use of multimodal ensembles of intelligent agents, they are being standardized internationally under the guidance of ISO. International standardization of multimodal ensembles of intelligent agents expands the market and reduces the risks of their use.

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

AI EOS using Wolframe Alpha

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops, and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language

Dynamic system models and their simulation in the Semantic Web

Modelling and Simulation (M&S) are core tools for designing, analysing and operating today’s industrial systems. They often also represent both a valuable asset and a significant investment. Typically, their use is constrained to a software environment intended to be used by engineers on a single computer. However, the knowledge relevant to a task involving modelling and simulation is in general distributed in nature, even across organizational boundaries, and may be large in volume. Therefore, it is desirable to increase the FAIRness (Findability, Accessibility, Interoperability, and Reuse) of M&S capabilities; to enable their use in loosely coupled systems of systems; and to support their composition and execution by intelligent software agents. In this contribution, the suitability of Semantic Web technologies to achieve these goals is investigated and an open-source proof of concept-implementation based on the Functional Mock-up Interface (FMI) standard is presented. Specifically, models, model instances, and simulation results are exposed through a hypermedia API and an implementation of the Pragmatic Proof Algorithm (PPA) is used to successfully demonstrate the API’s use by a generic software agent. The solution shows an increased degree of FAIRness and fully supports its use in loosely coupled systems. The FAIRness could be further improved by providing more “ rich” (meta)data.

Review of the Mathematical Model, Properties, Classes and other Features of Software Agent Development

Reviewed: features of the definition of an agent and a software agent, its dimensions and other components; models of software agents and its properties; classification of software agents by architecture, communication principles and agent communication languages (ACL), as well as existing platforms for their development (e.g., JADE, SPADE); multi-agent system (MAS); behavior types of SPADE software agent based on the example of one of the platforms (including for subsequent experiments to compare behaviors in terms of hardware resources usage). Minor adjustments have been made to the syntax of mathematical expressions describing the agent model, and a revision of the formalized definitions of agent property set has been proposed; a formalized description of the model of studied agent type is determined.

Advancements in Reinforcement Learning Algorithms for Autonomous Systems

Reinforcement learning, often known as RL, has developed as a strong paradigm to teach autonomous software agents to make choices in contexts that are both complicated and dynamic. This abstract investigates recent developments and uses of RL in a variety of fields, showing both its transformational potential and the constraints that it faces at present. Recent developments in reinforcement learning (RL) algorithms, in particular deep reinforcement learning (DRL), have made it possible to make major advancements in autonomous decision- making tasks. DRL algorithms can learn complicated representations of state-action spaces by using deep neural networks. This allows for more efficient exploration and exploitation methods to be implemented. Additionally, advancements in algorithmic enhancements, such as prioritized experience replay and distributional reinforcement learning, have improved the stability and sample efficiency of reinforcement learning algorithms, which has made it possible for these algorithms to be used in real-world applications. Robotics, autonomous cars, game playing, finance, and healthcare are just a few of the many fields that may benefit from the use of RL. In the field of robotics, reinforcement learning (RL) makes it possible for autonomous agents to learn how to navigate, manipulate, and move about in environments that are both complicated and unstructured. To improve both safety and efficiency on the road, autonomous cars make use of reinforcement learning (RL) to make decisions in dynamic traffic situations. In finance, RL algorithms are used for portfolio optimization, algorithmic trading, and risk management. These applications serve to improve investment techniques and decision-making procedures. Furthermore, in the field of healthcare, RL supports individualized treatment planning, clinical decision support, and medical image analysis, which enables physicians to provide patients with care that is specifically suited to their needs. Despite the promising improvements and applications, RL is still confronted with several difficulties that restrict its capacity to be widely adopted and scaled. Among these problems are the inefficiency of the sample, the trade-offs between exploration and exploitation, concerns about safety and dependability, and the need for explainability and interpretability in decision-making processes. To effectively address these difficulties, it is necessary to engage in collaborative efforts across several disciplines, conduct research on algorithmic developments, and establish extensive assessment frameworks (Anon, 2022).

Modeling and algorithmization of the pricing process in electronic commerce using software agents

Modeling and algorithmization of the pricing process in electronic commerce using software agents

Software Agent Simulation Design on the Efficiency of Food Delivery

Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning.