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Journal articles on the topic 'Intelligent agent'
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1
Luo, Yueming. "Descartes’ Intelligent Agent." International Journal of Social Science Studies 10, no. 6 (November 9, 2022): 75. http://dx.doi.org/10.11114/ijsss.v10i6.5731.
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Neither the natural science explanation about the agent nor the modern and postmodern philosophical interpretation of the agent (subject) have sounded the death knell of the metaphysical subject in the era of information revolution. The artificial intelligence technology represented by Microsoft Avatar Framework has spread to the creative fields such as music and literature, which are unique to human beings, showing a broad application prospect and arousing academic reflection on human subject. Despite the numerous advantages posed intelligent agents, it is neither desirable nor possible to pull out the subject’s power supply and give up AI or give up human social intelligence. Rethinking Descartes' theory of mind and body will lead to in-depth development of AI.
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Baylor, Amy. "Beyond Butlers: Intelligent Agents as Mentors." Journal of Educational Computing Research 22, no. 4 (June 2000): 373–82. http://dx.doi.org/10.2190/1ebd-g126-tfcy-a3k6.
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This article discusses pedagogical issues for intelligent agents to successfully serve as mentors for educational purposes. Broader issues about the nature or persona necessary for an intelligent agent as mentor are discussed, incorporating usability and human-computer interaction issues such as the anthropomorphic qualities of the agent and the social relationship between learner and agent. Overall, to be effective for learning, it is argued that there are three main requirements for agents as mentors: 1) regulated intelligence; 2) the existence of a persona; and 3) pedagogical control.
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Valckenaers, Paul, Bart Saint Germain, Paul Verstraete, Jan Van Belle, Hadeli, and Hendrik Van Brussel. "Intelligent Product = Intelligent Agent + Intelligent Being." IFAC Proceedings Volumes 41, no. 2 (2008): 13827–32. http://dx.doi.org/10.3182/20080706-5-kr-1001.02341.
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Alexander, Samuel Allen. "Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents." Journal of Artificial General Intelligence 10, no. 1 (January 1, 2019): 24–45. http://dx.doi.org/10.2478/jagi-2019-0003.
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Abstract Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg-Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.
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Szemes, P., K. Morioka, J. H. Lee, and H. Hashimoto. "Mobile Agent Control in Intelligent Space." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 144. http://dx.doi.org/10.1299/jsmermd.2004.144_1.
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ANASTASSAKIS, G., and T. PANAYIOTOPOULOS. "A SYSTEM FOR LOGIC-BASED INTELLIGENT VIRTUAL AGENTS." International Journal on Artificial Intelligence Tools 13, no. 03 (September 2004): 593–621. http://dx.doi.org/10.1142/s0218213004001703.
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Combination of logic-based artificial intelligence with virtual reality in intelligent agent systems is an approach not extensively sought after to date. It is our belief that significant gain is to be expected if the technical challenges involved are overcome. In this paper, we describe the mVlTAL intelligent agent system, which is our latest effort towards this direction. The system is a contemporary intelligent agent system with applications in numerous areas, including intelligent virtual environments and formal artificial intelligence research. The system focuses largely on logic-based approaches, which are present in almost every aspect of it, including modeling, knowledge representation, definition of agent behaviors and inter-agent communication. In addition, virtual manifestation of the world and agents is also an inherent characteristic of the system. The system, even if still in a development and evaluation stage, has already been employed in experimental and educational applications, demonstrating the potential benefits of such an approach.
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Tian, Xue Yong, Tian Qing Chang, Shao Hua Shi, Lei Zhang, and Yang Han. "Modeling of Test Resource Based on Multi-Agent and its Application in Intelligent Virtual Instrument." Advanced Materials Research 139-141 (October 2010): 1973–76. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1973.
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Because of the poor reconfigurability of the existing test resource models and the faultiness of the intelligent modeling methods in automatic test system, the modeling method based on Multi-Agent was studied. By analyzing the characters of test resources, the test resource models were classified as device model, adapter model and configuration model. The typical test resource model based on Multi-Agent with three layers and five agents was built. The structure and function of system management agent, model reconfiguration agent, function management agent, channel control agent and function realization agent in the model were analyzed. Then its application in Intelligent Virtual Instrument was introduced. This model based on Multi-Agent has good reconfigurability and intelligence, and it can be used to build the general-purpose and intelligent automatic test system.
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Wan, Jin Chang, and Tao Cheng. "Research on Multi-Agent and Storm-Hadoop Based on Cooperative Sensing Framework for Multiple Intelligent Manufacturing Agent." Applied Mechanics and Materials 864 (April 2017): 192–201. http://dx.doi.org/10.4028/www.scientific.net/amm.864.192.
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With the bottlenecks of traditional manufacturing technology becoming increasingly prominent, manufacturing industry need to adjust and upgrade the industrial structure, and intelligent manufacturing is the future development direction of manufacturing industry. According to the characteristics of complex, open system hierarchy structure and distributed multi agent of intelligent manufacturing, in order to solve the problem of information integration and collaborative processing of the organic elements of intelligent manufacturing collaborative perception process, it is necessary to give the autonomy of intelligent manufacturing entities to make it to form a fully functional agent, and these agents are connected to the communication network as a network node, so as to realize the data sharing and collaborative awareness processing function between the intelligent manufacturing body. Therefore, this article build the intelligent manufacturing system based the multi Agent and Storm-Hadoop distributed cluster framework through the research and analysis of distributed artificial intelligence and distributed cluster mechanism, and provides a collaborative framework based on multi Agent and Storm-Hadoop for information integration, data sharing, collaborative processing of collaboration awareness problem for multi manufacturing subjects of intelligent manufacturing. The simulation results shows that the cooperative sensing framework proposed in this paper can provide an effective information, data and knowledge processing mechanism and method for realizing multi subject intelligent manufacturing collaborative awareness.
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Yu, Yan, and Jian Hua Wang. "Research on Network Examination System Model Based on Multi-AGENT." Advanced Materials Research 566 (September 2012): 685–90. http://dx.doi.org/10.4028/www.scientific.net/amr.566.685.
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With the development of artificial intelligence, Multi-agent technology is applied more and more in reality. In line with multi-user intelligent network examination test system, this paper proposes multi-agent-based general examination system model constituted by multiple mutually independent but collaborated AGENTs. In addition to fulfill their respective responsibilities, every single sub-Agent communicates among each other to gain information and complete missions in a collaborative form. Also, dedicated data mining AGENT is deployed in the system to conduct intelligent analysis and processing on various data, thereby providing decision support for teachers’ teaching and student’s study duties. And this paper presents a new clue for establishing intelligent network examination system under distributed environment.
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Bryndin, Evgeny. "Functional and Harmonious Selforganization of Large Intellectual Agent Ensembles with Smart Hybrid Competencies via Wireless and Mobile Networks." International Journal of Wireless & Mobile Networks 13, no. 05 (October 31, 2021): 1–13. http://dx.doi.org/10.5121/ijwmn.2021.13501.
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Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. The article is devoted to functional harmonious selforganization of ensembles of intellectual agents.Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out via wireless and mobile networks on the basis of data of a specific environment obtained by analytical competent intellectual agents. As result of research, the law of the golden section of the functional harmonious self-organization of ensembles of intellectual agents was revealed. Further research will focus on the effective use of wireless and mobile networks in the practical application of smart agent ensembles.
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CHOI, JEONGEUN, and BYOUNGJU CHOI. "TEST AGENT SYSTEM." International Journal of Software Engineering and Knowledge Engineering 12, no. 03 (June 2002): 269–90. http://dx.doi.org/10.1142/s021819400200086x.
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In this paper, we propose a Test Agent System (TAS) that employs intelligent agent characteristics of autonomy, social ability, and intelligence to provide active assistance to the tester. It consists of the 'User Interface Agent', the 'Test Case Selection & Testing Agent', and the 'Regression Test Agent'. These agents carry out the tests autonomously by employing their intelligent rules in the object-oriented test processes. This system has 2 advantages. Firstly, since the tests are carried out autonomously, it minimizes tester interference and secondly, since redundant-free and consistent effective test cases are intellectually selected, the testing time is reduced while the fault detection effectiveness improves. In this paper, we show the cut-down rate on the testing time and improvement in the fault detection effectiveness through experiments.
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Ioannidis, Evangelos, Nikos Varsakelis, and Ioannis Antoniou. "Intelligent Agents in Co-Evolving Knowledge Networks." Mathematics 9, no. 1 (January 5, 2021): 103. http://dx.doi.org/10.3390/math9010103.
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We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our network co-evolution model is actually a “learning mechanism”, where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size. We find that rational intelligent agents transform the network into a “centralized world”, reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge, as well as the “knowledge inequality”, grow exponentially.
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Putro, Budi Laksono, Yusep Rosmansyah, and Suhardi Suhardi. "An intelligent agent model for learning group development in the digital learning environment: A systematic literature review." Bulletin of Electrical Engineering and Informatics 9, no. 3 (June 1, 2020): 1159–66. http://dx.doi.org/10.11591/eei.v9i3.2009.
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Group development is the first and most important step for the success of collaborative problem solving (CPS) learning in the digital learning environment (DLE). A literacy study is needed for studies in the intelligent agent domain for group development of collaborative learning in DLE. This paper is a systematic literature review (SLR) of intelligent agents for group formation from 2001 to 2019. This paper aims to find answers to 4 (four) research questions, namely: 1) What components to develop intelligent agents for group development; 2) What is the intelligent agent model for group development; 3) How are the metrics for measuring intelligent agent performance; and 4) How is the Framework for developing intelligent agent. The components of the intelligent agent model consist of: member attributes, group attributes (group constraints), and intelligent techniques. This research refers to Srba and Bielikova's group development model. The stages of the model are formation, performing and closing. An intelligent agent model at the formation stage. A performance metric for the intelligent agent at the performance stage. The framework for developing an intelligent agent is a reference to the stages of development, component selection techniques, and performance measurement of an intelligent agent.
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Alsetoohy, Omar, and Baker Ayoun. "Intelligent agent technology." Journal of Hospitality and Tourism Technology 9, no. 1 (March 12, 2018): 109–24. http://dx.doi.org/10.1108/jhtt-04-2017-0028.
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Purpose This paper aims to empirically investigate the influence of the intelligent agent technology (IAT) on hotels’ food procurement practices (FPPRs) and food procurement performance (FPP). The study also explores the influence of FPPR on FPP in hotels. Design/methodology/approach Data from a survey of managers at luxury hotels in Florida, which yielded an effective sample of 78, were subjected to partial least squares structural equation modeling analysis. Findings IAT usage has statistically significant positive effect on both FPPR and FPP. Additionally, the results indicated that PPR has a positive influence on FPP. Research limitations/implications The study provided academia with a comprehensive review of the prior research on IAT benefits in food supply chain management (FSCM) and a comprehensive explanation of how the hotel FPPR are being performed by IAT. It also offers industry and technology specific insights. Practical implications Hotel managers should pay more attention to IAT application regarding the FPPR, specifically gathering information, supplier contract, contacting, requisitioning and intelligence and analysis. Moreover, hotel managers should enhance their employees’ abilities to be technology-oriented to maximize IAT benefits in FPP. Originality/value The results represent an extension to the technological innovation literature of the hospitality industry to bridge the gap of IAT in hotel FP. The framework used in the study serves as a foundation for future research studies in the hospitality industry, in FSCM in particular.
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Stowers, Kimberly, Nicholas Kasdaglis, Olivia Newton, Shan Lakhmani, Ryan Wohleber, and Jessie Chen. "Intelligent Agent Transparency." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1706–10. http://dx.doi.org/10.1177/1541931213601392.
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We evaluated the usability and utility of an unmanned vehicle management interface that was developed based on the Situation awareness–based Agent Transparency model. We sought to examine the effect of increasing levels of agent transparency on operator task performance and perceived usability of the agent. Usability and utility were assessed through flash testing, a focus group, and experimental testing. While usability appeared to decrease with the portrayal of uncertainty, operator performance and reliance on key parts of the interface increased. Implications and next steps are discussed.
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Dubenko, Yu V. "ANALYTICAL REVIEW OF MULTI-AGENT REINFORCEMENT LEARNING PROBLEMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 192 (June 2020): 48–56. http://dx.doi.org/10.14489/vkit.2020.06.pp.048-056.
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This paper is devoted to the problem of collective artificial intelligence in solving problems by intelligent agents in external environments. The environments may be: fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous. The paper identifies problems of collective interaction of intelligent agents when they solve a class of tasks, which need to coordinate actions of agent group, e. g. task of exploring the territory of a complex infrastructure facility. It is revealed that the problem of reinforcement training in multi-agent systems is poorly presented in the press, especially in Russian-language publications. The article analyzes reinforcement learning, describes hierarchical reinforcement learning, presents basic methods to implement reinforcement learning. The concept of macro-action by agents integrated in groups is introduced. The main problems of intelligent agents collective interaction for problem solving (i. e. calculation of individual rewards for each agent; agent coordination issues; application of macro actions by agents integrated into groups; exchange of experience generated by various agents as part of solving a collective problem) are identified. The model of multi-agent reinforcement learning is described in details. The article describes problems of this approach building on existing solutions. Basic problems of multi-agent reinforcement learning are formulated in conclusion.
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Dubenko, Yu V. "ANALYTICAL REVIEW OF MULTI-AGENT REINFORCEMENT LEARNING PROBLEMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 192 (June 2020): 48–56. http://dx.doi.org/10.14489/vkit.2020.06.pp.048-056.
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This paper is devoted to the problem of collective artificial intelligence in solving problems by intelligent agents in external environments. The environments may be: fully or partially observable, deterministic or stochastic, static or dynamic, discrete or continuous. The paper identifies problems of collective interaction of intelligent agents when they solve a class of tasks, which need to coordinate actions of agent group, e. g. task of exploring the territory of a complex infrastructure facility. It is revealed that the problem of reinforcement training in multi-agent systems is poorly presented in the press, especially in Russian-language publications. The article analyzes reinforcement learning, describes hierarchical reinforcement learning, presents basic methods to implement reinforcement learning. The concept of macro-action by agents integrated in groups is introduced. The main problems of intelligent agents collective interaction for problem solving (i. e. calculation of individual rewards for each agent; agent coordination issues; application of macro actions by agents integrated into groups; exchange of experience generated by various agents as part of solving a collective problem) are identified. The model of multi-agent reinforcement learning is described in details. The article describes problems of this approach building on existing solutions. Basic problems of multi-agent reinforcement learning are formulated in conclusion.
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Dushkin, Roman. "Multi-agent systems for cooperative ITS." Тренды и управление, no. 1 (January 2021): 42–50. http://dx.doi.org/10.7256/2454-0730.2021.1.34169.
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This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.
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Kirikov, I. A., S. V. Listopad, and A. S. Luchko. "Agents’ ontologies negotiation in cohesive hybrid intelligent multi-agent systems." Journal of Physics: Conference Series 2094, no. 3 (November 1, 2021): 032033. http://dx.doi.org/10.1088/1742-6596/2094/3/032033.
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Abstract The paper proposes the model for negotiating intelligent agents’ ontologies in cohesive hybrid intelligent multi-agent systems. Intelligent agent in this study will be called relatively autonomous software entity with developed domain models and goal-setting mechanisms. When such agents have to work together within single hybrid intelligent multi-agent systems to solve some problem, the working process “go wild”, if there are significant differences between the agents’ “points of view” on the domain, goals and rules of joint work. In this regard, in order to reduce labor costs for integrating intelligent agents into a single system, the concept of cohesive hybrid intelligent multi-agent systems was proposed that implement mechanisms for negotiating goals, domain models and building a protocol for solving the problems posed. The presence of these mechanisms is especially important when building intelligent systems from intelligent agents created by various independent development teams.
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Petrovica, Sintija, and Alla Anohina-Naumeca. "Design and implementation of agent interaction mechanisms for emotionally intelligent tutoring systems." Applied Computer Systems 13, no. 1 (November 8, 2012): 44–53. http://dx.doi.org/10.2478/v10312-012-0006-2.
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Abstract - Modern intelligent systems are built from intelligent agents, but usually agent interaction mechanisms are described at the general level omitting design and implementation details. The paper presents aspects of practical implementation of agent interaction mechanisms in JADE platform on the basis of the set of agents for modelling student’s emotions in intelligent tutoring systems. The concepts of an intelligent and affective tutoring system, agent, and agent interaction are explained, as well.
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Cardoso, Rafael C., and Angelo Ferrando. "A Review of Agent-Based Programming for Multi-Agent Systems." Computers 10, no. 2 (January 27, 2021): 16. http://dx.doi.org/10.3390/computers10020016.
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Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.
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Tang, Jin Peng, Ling Lin Li, Shu Kun Liu, De Peng Hu, Me Ling Cai, and Xiao Qiu Liu. "Research of Intersection Navigation Algorithm Based on Intelligent Agent." Applied Mechanics and Materials 641-642 (September 2014): 690–93. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.690.
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In order to improve the vehicle alone path finding way, put forward a kind of road navigation algorithm based on agent. First put forward the intelligent design method, and then the intelligent design is mapped to different kinds of the intelligent agents. And then expounds the function of operation control agent, agent, Lane agent and intersection agent needs to realize. Finally, based on these agents of the navigation algorithm is designed using Dijkstra algorithm.
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Bryndin, Evgeny. "Intellectual Agent Ensemble with Professional Competencies, Pattern Recognition and Decision Making." Applied Science and Innovative Research 6, no. 4 (September 8, 2022): p1. http://dx.doi.org/10.22158/asir.v6n4p1.
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Each competence is exercised by an intellectual agent with a competent functional professional image. Intellectual agents form an ensemble with clever ethical artificial intelligence. The use of an ensemble with intelligent ethical artificial intelligence in various environments is carried out by synergistically adjusting the interaction of intelligent agents based on data from a specific environment obtained by an analytical competent intellectual agent. Modeling holographic processes of the human psyche based on artificial intelligence of machine learning with Fourier transformation using full parametric sequences of necessary and sufficient data of holograms of target objects solves the problem of their unambiguous detection in different environments and in different conditions. An ensemble of intelligent decision-making agents is a cognitive information system that makes a decision based on an objective analysis of available data in difficult situations, in an interactive mode, taking into account performance criteria and resource-time constraints. Decision-making criteria are functionalities that express preferences and allow ranking the quality of decisions. Decisions are made on the basis of rules. Decision rules are a set of logical constructs used to produce a decision based on criteria, data, and knowledge. An ensemble of intellectual agents with professional competencies, pattern recognition and decision-making fully model the abilities of the human psyche.
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Sabitha, R., and Karthik. "EMPLOYING AGENTS IN DESCRIPTIVE MINING." International Journal of Research -GRANTHAALAYAH 4, no. 2 (February 29, 2016): 111–20. http://dx.doi.org/10.29121/granthaalayah.v4.i2.2016.2821.
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Agent technology and Data Mining have emerged as two of the prominent areas in information sciences. An effort has been activated towards the interaction and integration between agent technology and data mining which is referred to as “AGENT MINING”. Data Mining is the process of extracting interesting information or patterns from large volumes of data. Agents comprise a powerful technology for the analysis, design and implementation of autonomous intelligent systems that can handle distributed problem-solving, cooperation, coordination, communication, and organization in a multiplayer environment. This agent uses information technology to find trends and patterns in an abundance of information from many different sources. The user can sort through this information in order to find whatever information they are seeking. Intelligent agents are today accepted as powerful tools for data mining in a distributed environment. The interaction and integration between agent and mining has potential to not only strengthen either side, but generate new techniques for developing more powerful intelligence and intelligent information processing systems. This paper discusses how agents are used in the various descriptive models of Data Mining. The various challenges and methodologies are analyzed and it clearly indicates the need for and the promising potential of agent mining for the mutual enhancement of both fields and for the creation of super-intelligent systems. Even though many researchers have been committed, more efforts are required to develop techniques and systems in practical perspectives.
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Brown, Ryan Anthony, and Suresh Sankaranarayanan. "Intelligent Store Agent for Mobile Shopping." International Journal of E-Services and Mobile Applications 3, no. 1 (January 2011): 57–72. http://dx.doi.org/10.4018/jesma.2011010104.
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The conventional shopping process involves a human being visiting a designated store and perusing first the items available. A purchase decision is then made based on the information so gathered. However, a number of unique challenges a human shopper would face, if he/she prefers to execute this process using a mobile device, such as a phone. Taking this aspect into consideration, the authors propose the use of an Intelligent Agent for performing the Mobile Shopping on behalf of customers. In this situation, the agents gather information about the products through the use of ‘Store Coordinator Agents’ and then use them for comparing with the user preferences. The proposed agent based system is composed of two agents, viz., a User Agent and Store Coordinator Agent. The implementation of the scheme so proposed has been done using JADE-LEAP development kit and the performance results are discussed in the paper.
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LEE, RAYMOND S. T. "iJADE AUTHENTICATOR — AN INTELLIGENT MULTIAGENT BASED FACIAL AUTHENTICATION SYSTEM." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 04 (June 2002): 481–500. http://dx.doi.org/10.1142/s0218001402001794.
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In modern consumer e-shopping environments, customer authentication is a critical process for confirming the identity of the customer. Traditional authentication techniques that rely on the customers to proactively identify themselves (using various schemes) can affect the user-friendliness of the e-shopping experience, and therefore reduce the customers' preference for such facilities. In this paper, we propose an innovative intelligent multiagent-based environment, called iJADE (intelligent Java Agent Development Environment) to provide an intelligent agent-based platform in the e-commerce environment. Contemporary agent development platforms are focused on the autonomy and mobility of the agents, whereas iJADE provides an intelligent layer (known as the "conscious layer") to implement various AI (artificial intelligence) functionalities in order to produce "smart" agents. From an implementation perspective, we introduce an innovative e-shopping authentication scheme called the "iJADE Authenticator", which is an invariant face recognition system that uses intelligent mobile agents. This system can provide fully automatic, mobile and reliable user authentication. More importantly, the authentication process can be carried out without the users necessarily being aware of it. Experimental results are presented for a database of 1020 tested face images obtained under conditions of widely varying facial expressions, viewing perspectives and image sizes. An overall average correct recognition rate of over 90% is attained.
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Evgeny Bryndin. "Multi-agent Intelligent Ensembles with Professional Competencies, Pattern Recognition and Decision Making." Britain International of Exact Sciences (BIoEx) Journal 4, no. 3 (September 16, 2022): 172–81. http://dx.doi.org/10.33258/bioex.v4i3.752.
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Each competence is exercised by an intellectual agent with a competent functional professional image. Intellectual agents form an ensemble with clever ethical artificial intelligence. The use of an ensemble with intelligent ethical artificial intelligence in various environments is carried out by synergistically adjusting the interaction of intelligent agents based on data from a specific environment obtained by an analytical competent intellectual agent. Modeling holographic processes of the human psyche based on artificial intelligence of machine learning with Fourier transformation using full parametric sequences of necessary and sufficient data of holograms of target objects solves the problem of their unambiguous detection in different environments and in different conditions. An ensemble of intelligent decision-making agents is a cognitive information system that makes a decision based on an objective analysis of available data in difficult situations, in an interactive mode, taking into account performance criteria and resource-time constraints. Decision-making criteria are functionalities that express preferences and allow ranking the quality of decisions. Decisions are made on the basis of rules. Decision rules are a set of logical constructs used to produce a decision based on criteria, data, and knowledge. An ensemble of intellectual agents with professional competencies, pattern recognition and decision-making fully model the abilities of the human psyche.
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Wooldridge, Michael, and Nicholas R. Jennings. "Intelligent agents: theory and practice." Knowledge Engineering Review 10, no. 2 (June 1995): 115–52. http://dx.doi.org/10.1017/s0269888900008122.
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AbstractThe concept of anagenthas become important in both artificial intelligence (AT) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary).Agent theoryis concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents.Agent architecturescan be thought of as software engineering models of agents; researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally,agent languagesare software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper isnotintended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.
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Baylor, Amy L. "Agent-Based Learning Environments as a Research Tool for Investigating Teaching and Learning." Journal of Educational Computing Research 26, no. 3 (April 2002): 227–48. http://dx.doi.org/10.2190/ph2k-6p09-k8ec-krdk.
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By using intelligent agents to simulate instruction, agent-based learning environments can serve as a powerful research tool to investigate teaching and learning. The agent metaphor provides a way to operationalize and simulate the “human” aspect of instruction in a more ecologically valid way than other controlled computer-based methods. Additionally, from an architectural perspective, since agents are independent objects in the learning environment, it allows for more flexibility in research design. In particular, agent-based learning environments with multiple agents, such as MIMIC (Multiple Intelligent Mentors Instructing Collaboratively), allow for investigating the effect of multiple mentors or multiple perspectives on a learning topic. Preliminary results from MIMIC research indicate that multiple agents can serve to effectively operationalize instructional theory. In terms of overall impact, creating agent-based learning environments to investigate instructional issues is at the leading edge of revitalized research integrating artificial intelligence with education, and in exploring new paradigms for researching teaching and learning.
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Chen, Xiu Sheng, Cheng Rui Zhang, Shu Kun Cao, Ri Liang Liu, Qiang Wang, and Hong Bo Lan. "Intelligent Control of CNC System Based on STEP-NC." Applied Mechanics and Materials 16-19 (October 2009): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.915.
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In this paper, Data model used in intelligent CNC system based on STEP-NC is presented. A multi-agent architecture of the intelligent CNC system based on STEP-NC is proposed. The architecture consists of fourteen functional agents, such as interpreter agent, process planning agent, simulation agent, tool path planning agent, monitoring agent, expert system agent, etc. Some key technologies to implement the intelligent control including simulation, online inspection and expert system are discussed detailed. As a result, the architecture and key technologies proposed in this paper will aid the development work of the intelligent STEP-NC system.
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Patel, Shailesh A., and Dr Jayesh M. Patel. "Web Crawler: An Intelligent Agent Through Intellect Webbot." Indian Journal of Applied Research 1, no. 12 (October 1, 2011): 45–48. http://dx.doi.org/10.15373/2249555x/sep2012/16.
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Lavendelis, Egons, and Janis Grundspenkis. "Design of Multi-Agent Based Intelligent Tutoring Systems." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 48–59. http://dx.doi.org/10.2478/v10143-009-0004-z.
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Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.
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Liu, Qiang, Chao Chen, and Shanben Chen. "Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent." Journal of Manufacturing and Materials Processing 6, no. 6 (November 5, 2022): 135. http://dx.doi.org/10.3390/jmmp6060135.
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With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems.
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GERO, JOHN S., and FRANCES M. T. BRAZIER. "Special Issue: Intelligent agents in design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 113. http://dx.doi.org/10.1017/s0890060404040089.
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This Special Issue had its genesis in an international Workshop on Agents in Design held in June 2002, at MIT by the Guest Editors. Computational agents have been developed within the artificial intelligence community over an extended period. The concept of an agent can be traced to Carl Hewitt's 1977 work on “actors.” Hewitt defined actors as self-contained, interactive, and concurrently executing objects. Since then, considerable research has gone into developing the concept of an agent and into formalizing agents, developing multiagent systems, and exploring their use. The use of agents in design is more recent, and the first PhDs in the area appeared in the early 1990s. Although a precise and unique definition of an agent has yet to be agreed upon, one distinguishing characteristic of an agent is that it exhibits autonomous behavior. Research on agents in design focuses on two primary areas: how to make agents useful in design, and how to apply them to design tasks. This Special Issue has papers from both areas.
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Mallory, Fintan. "In Defence of a Reciprocal Turing Test." Minds and Machines 30, no. 4 (December 2020): 659–80. http://dx.doi.org/10.1007/s11023-020-09552-5.
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AbstractThe traditional Turing test appeals to an interrogator's judgement to determine whether or not their interlocutor is an intelligent agent. This paper argues that this kind of asymmetric experimental set-up is inappropriate for tracking a property such as intelligence because intelligence is grounded in part by symmetric relations of recognition between agents. In place, it proposes a reciprocal test which takes into account the judgments of both interrogators and competitors to determine if an agent is intelligent. This form of social interaction better tracks both the evolution of natural intelligence and how the concept of intelligence is actually used within our society. This new test is defended against the criticisms that a proof of intelligence requires a demonstration of self-consciousness and that semantic externalism entails that a non-embodied Turing test is inadequate.
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MÜLLER, JÖRG P. "Architectures and applications of intelligent agents: A survey." Knowledge Engineering Review 13, no. 4 (February 1999): 353–80. http://dx.doi.org/10.1017/s0269888998004020.
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The objective of this paper is twofold. In its first part, we survey the state of the art in research on agent architectures. The architecture of an agent describes its modules and capabilities, and how these operate together. We structure the field by investigating three important research threads, i.e. architectures for reactive agents, deliberative agents and interacting agents. Then we describe various hybrid approaches that reconcile these three threads, aiming at a combination of different features like reactivity, deliberation and the ability to interact with other agents. These approaches are contrasted with architectural issues of recent agent-based work, including software agents, softbots, believable agents, as well as commercial agent-based systems. The second part of the paper addresses software engineers and system designers who are interested in applying agent technology to their problem domains. The objective of this part is to assist these readers in deciding which agent architecture to choose for a specific application. We characterise the most important domains to which the different approaches described in the first part have been applied, propose an application-related taxonomy of agents, and give a set of guidelines to select the right agent (architecture) for a given application.
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Krening, Samantha, and Karen M. Feigh. "Characteristics that Influence Perceived Intelligence in AI Design." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1637–41. http://dx.doi.org/10.1177/1541931218621371.
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A goal of interactive machine learning (IML) is to create robots or intelligent agents that can be easily taught how to perform tasks by individuals with no specialized training. To achieve that goal, researchers and designers must understand how certain design decisions impact the human’s experience of teaching the agent, such as influencing the agent’s perceived intelligence. We posit that the type of feedback a robot can learn from affects the perceived intelligence of the robot, similar to its physical appearance. This study investigated two methods of natural language instruction: critique and action advice. We conducted a human-in-the-loop experiment in which people trained two agents with different teaching methods but, unknown to each participant, the same underlying machine learning algorithm. The results show an agent that learns from binary good/bad critique is perceived as less intelligent than an agent that can learn from action instructions, even if the underlying machine learning agent is the same. In addition to the complexity of the input, other design characteristics we found that influence the agent’s perceived intelligence are: compliance, responsiveness, effort, transparency, and robustness.
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Schleiffer, Ralf. "An intelligent agent model." European Journal of Operational Research 166, no. 3 (November 2005): 666–93. http://dx.doi.org/10.1016/j.ejor.2004.03.039.
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Wang, Manhua, Phillip Hock, Seul Chan Lee, Martin Baumann, and Myounghoon Jeon. "Jarvis in the car: Report on characterizing and designing in-vehicle intelligent agents workshop." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 948–52. http://dx.doi.org/10.1177/1071181322661445.
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As intelligent agents have become more popular at home, they have been progressively introduced into driving environments. Although previous research has discussed agent features and their effects on driver perception and performance, attributes that define in-vehicle agents and distinguish them from other intelligent agents have not been discussed clearly. Thus, we organized a workshop on characterizing and designing in-vehicle intelligent agents at the 13th International Conference on Automotive User Interfaces (AutoUI 2021). In this report, we integrated ideas generated during the workshop and identified user-centered action and autonomy as two attributes that define an agent, with functions and features as specific characteristics that vary agent design. The outcomes of this workshop can facilitate in-vehicle intelligent agent design and deliver optimal user experience, while providing insights on manipulating variables in controlled studies.
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Soliman, Mohamed, and Christian Guetl. "Evaluation of Intelligent Agent Frameworks for Human Learning." International Journal of Engineering Pedagogy (iJEP) 1, no. 3 (October 10, 2011): 45. http://dx.doi.org/10.3991/ijep.v1i3.1816.
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<p align="left"> </p><p><strong><span style="font-size: xx-small;">Pedagogical Agents are intelligent agents supporting learning in virtual learning environments, VLE. The use of the multi-agent society model inhabited with intelligent virtual agents has shown to provide several benefits to learning. This paper reviews intelligent agents for learning and shows their educational value while demonstrating the new learning possibilities supported by them. Towards the objective of efficiently utilizing the agents in a distributed learning platform, the paper provides an evaluation of intelligent agent development frameworks. This evaluation will provide valuable information to those employing and integrating intelligent agents for different types of VLE with a view towards creating new learning scenarios </span></strong></p>
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Yu, Yan, and Jian Hua Wang. "The Study on the Key Technologies in Multiple Agent-Based Intelligent Tutoring System." Advanced Materials Research 846-847 (November 2013): 1889–92. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1889.
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Network teaching system has changed the way of teaching, with the artificial intelligence technology, Currently, many traditional network teaching system has been unable to meet the needs of the public.After studying the defects in current teaching systems and combining features of multi-agent systems and their application theories in intelligent teaching systems, this paper, integrating the concept of multi-agent systems, discusses the key technologies in multiple agent-based intelligent tutoring systems based on multiple agent-based intelligent tutoring system models. To achieve true intelligent teaching system, simulate real teaching environments, offer customized teaching services, and really realize students' independent learning and collaborative learning.
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VIROLI, MIRKO, ALESSANDRO RICCI, and ANDREA OMICINI. "Operating instructions for intelligent agent coordination." Knowledge Engineering Review 21, no. 1 (March 2006): 49–69. http://dx.doi.org/10.1017/s0269888906000774.
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In contrast to standard approaches based on agent communication languages (ACLs), environment-based coordination is emerging as an interesting alternative for structuring interactions in multiagent systems (MASs). In particular, the notion of coordination artifacts has been proposed as an engineering methodology to build runtime abstractions effectively providing collaborating agents with specifically designed coordination tasks.In this paper, we study the semantics for the interaction of agents with coordination artifacts playing the same role of ACL semantics, that is, supporting semantic interoperability between agents developed by different parties through the connection between rationality and interaction. Our approach is rooted on the notion of operating instructions of coordination artifacts, which—as with a manual for a human exploiting a device—describe the interaction protocols the agent can follow as well as the mentalistic semantics of each single interaction. By tackling some of the most relevant issues raised in the context of ACL semantics, our framework allows intelligent, BDI-like agents to carry on complex interactions through coordination artifacts in a rational way.
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Wang, Jian Hua, Yan Yu, and Jun Jie Guo. "The Study on the Multiple Agent-Based Independent and Collaborative Intelligent Tutoring System Model." Advanced Materials Research 846-847 (November 2013): 1885–88. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1885.
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Over the years, the traditional computer-assisted teaching can not meet the needs of university teaching, the traditional teaching system software, most of the existence of low intelligence, lack of teaching strategies and other shortcomings.Multi-AGENT technology and intelligent tutoring systems is the current research focus in computer intelligence education. Integrating multi-Agent features and multi-Agent application theories in ITS, this paper proposes a multiple Agent-based intelligent network tutoring system design model, detailedly analyzes the functions of each layer in the system, and presents system database category design and system model features.
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SUKONSCHIKOV, ALEXEY A., ANATOLY N. SHVETSOV, IGOR A. ANDRIANOV, and DMITRIY V. KOCHKIN. "PRINCIPLES OF BUILDING SELF-ORGANIZING INFORMATION AND TELECOMMUNICATION SYSTEMS." Cherepovets State University Bulletin 1, no. 100 (2021): 56–67. http://dx.doi.org/10.23859/1994-0637-2021-1-100-4.
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The article discusses the basic foundations of the functioning and evolution of complex distributed information and telecommunication systems (ITS) in the neuro-fuzzy agent-based paradigm. The formalization principles for such systemsareproposed:goal structuring, agent orientation, hierarchical intellectual organization, modularity of intelligent agents, structural and functional flexibility of intelligent agents, single mathematical apparatus, self-development and system evolution.The concept of a distributed intelligent ITS as a geographically distributed multi-level hierarchical set of hardware and software controlled by agent-based systems is introduced.
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Chu, Junchi, Xueyun Tang, and Xiwei Shen. "A Design Framework of Exploration, Segmentation, Navigation, and Instruction (ESNI) for the Lifecycle of Intelligent Mobile Agents as a Method for Mapping an Unknown Built Environment." Sensors 22, no. 17 (September 1, 2022): 6615. http://dx.doi.org/10.3390/s22176615.
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Recent work on intelligent agents is a popular topic among the artificial intelligence community and robotic system design. The complexity of designing a framework as a guide for intelligent agents in an unknown built environment suggests a pressing need for the development of autonomous agents. However, most of the existing intelligent mobile agent design focus on the achievement of agent’s specific practicality and ignore the systematic integration. Furthermore, there are only few studies focus on how the agent can utilize the information collected in unknown build environment to produce a learning pipeline for fundamental task prototype. The hierarchical framework is a combination of different individual modules that support a type of functionality by applying algorithms and each module is sequentially connected as a prerequisite for the next module. The proposed framework proved the effectiveness of ESNI system integration in the experiment section by evaluating the results in the testing environment. By a series of comparative simulations, the agent can quickly build the knowledge representation of the unknown environment, plan the actions accordingly, and perform some basic tasks sequentially. In addition, we discussed some common failures and limitations of the proposed framework.
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Sangiacomo, Andrea. "Samuel Clarke on Agent Causation, Voluntarism, and Occasionalism." Science in Context 31, no. 4 (December 2018): 421–56. http://dx.doi.org/10.1017/s0269889718000340.
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ArgumentThis paper argues that Samuel Clarke's account of agent causation (i) provides a philosophical basis for moderate voluntarism, and (ii) both leads to and benefits from the acceptance of partial occasionalism as a model of causation for material beings. Clarke's account of agent causation entails that for an agent to be properly called an agent (i.e. causally efficacious), it is essential that the agent is free to choose whether to act or not. This freedom is compatible with the existence of conceptually necessary connections. Hence, Clarke can harmonize God's freedom of choosing with the existence of eternal and necessary relations among things. Moreover, in Clarke's account, only intelligent entities can be properly understood as efficacious causes. Beings deprived of intelligence are not agents or efficacious causes at all and their effects are thus the result of the immediate action of some intelligent being operating upon them.
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Wu, Zhao Dan, Chang Feng Shi, and Yi Lu. "Intelligent Information Retrieval System Based on Muti-Agent Model." Advanced Materials Research 765-767 (September 2013): 1529–32. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1529.
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The intelligent information retrieval model discussed in this paper is constructed by multi-agent. Currently, BDI cognitive theory is accepted widely by the scholars of this field, but the related researches mainly focus on the theoretical derivation and presentation of symbols, lack of model facing practical application. In this article, a dynamic rule-based reasoning model is proposed. The model based on BDI theory is an expression of agent intelligence. The basic logical reasoning of the BDI theory is extended in this article. The author not only introduces several functions to study the dynamic changes of agents mental state, but also give a detailed description of how to use the theory of production rules to express BDI-based reasoning. This article also studies and designs the agent communication mechanism in MAS. Finally, the intelligent information retrieval system is designed and implemented with the idea of AOP.
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Tang, Wei, and Jun Lai. "Enhancing Agent Intelligence with Behavior Duplication." Advanced Materials Research 403-408 (November 2011): 1266–69. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1266.
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The traditional agent intelligence designing always lead to a fixed behavior manner. In this way, the NPC(Non-Player Character) in the game will act in a fixed and expectable way. It has greatly weakened the long-term attraction of single-played game. Extracting the human action patterns using a statistical-based machine learning algorithm can provide an easily-understanding way to implement the agent behavior intelligence. A daemon program records and sample the human player’s input action and related properties of character and virtual environment, and then apply certain statistical-based machine learning algorithm on the sample data. As a result, a human-similar intelligent behavior model was obtained. It can be used to help agent making an action decision. Repeating the learning process can give the agent a variety of intelligent behavior.
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Ji, Wen, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen. "Quality-time-complexity universal intelligence measurement." International Journal of Crowd Science 2, no. 1 (March 5, 2018): 18–26. http://dx.doi.org/10.1108/ijcs-01-2018-0003.
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Purpose With development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more and more autonomous and smart. Therefore, there is a growing demand to develop a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method. Design/methodology/approach This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents. Findings By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment. Practical implications In a crowd network, a number of intelligent agents are able to collaborate with each other to finish a certain kind of sophisticated tasks. The proposed approach can be used to allocate the tasks to the agents within a crowd network in an optimized manner. Originality/value This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.
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Neururer, Mario, Stephan Schlögl, Luisa Brinkschulte, and Aleksander Groth. "Perceptions on Authenticity in Chat Bots." Multimodal Technologies and Interaction 2, no. 3 (September 17, 2018): 60. http://dx.doi.org/10.3390/mti2030060.
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In 1950, Alan Turing proposed his concept of universal machines, emphasizing their abilities to learn, think, and behave in a human-like manner. Today, the existence of intelligent agents imitating human characteristics is more relevant than ever. They have expanded to numerous aspects of daily life. Yet, while they are often seen as work simplifiers, their interactions usually lack social competence. In particular, they miss what one may call authenticity. In the study presented in this paper, we explore how characteristics of social intelligence may enhance future agent implementations. Interviews and an open question survey with experts from different fields have led to a shared understanding of what it would take to make intelligent virtual agents, in particular messaging agents (i.e., chat bots), more authentic. Results suggest that showcasing a transparent purpose, learning from experience, anthropomorphizing, human-like conversational behavior, and coherence, are guiding characteristics for agent authenticity and should consequently allow for and support a better coexistence of artificial intelligence technology with its respective users.