Zeitschriftenartikel: „Intelligent agents (computer software)“

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Odhiambo, M. O., and P. O. Umenne. "NET-COMPUTER: Internet Computer Architecture and its Application in E-Commerce." Engineering, Technology & Applied Science Research 2, no. 6 (December 4, 2012): 302–9. http://dx.doi.org/10.48084/etasr.145.

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Research in Intelligent Agents has yielded interesting results, some of which have been translated into commercial ventures. Intelligent Agents are executable software components that represent the user, perform tasks on behalf of the user and when the task terminates, the Agents send the result to the user. Intelligent Agents are best suited for the Internet: a collection of computers connected together in a world-wide computer network. Swarm and HYDRA computer architectures for Agents’ execution were developed at the University of Surrey, UK in the 90s. The objective of the research was to develop a software-based computer architecture on which Agents execution could be explored. The combination of Intelligent Agents and HYDRA computer architecture gave rise to a new computer concept: the NET-Computer in which the computing resources reside on the Internet. The Internet computers form the hardware and software resources, and the user is provided with a simple interface to access the Internet and run user tasks. The Agents autonomously roam the Internet (NET-Computer) executing the tasks. A growing segment of the Internet is E-Commerce for online shopping for products and services. The Internet computing resources provide a marketplace for product suppliers and consumers alike. Consumers are looking for suppliers selling products and services, while suppliers are looking for buyers. Searching the vast amount of information available on the Internet causes a great deal of problems for both consumers and suppliers. Intelligent Agents executing on the NET-Computer can surf through the Internet and select specific information of interest to the user. The simulation results show that Intelligent Agents executing HYDRA computer architecture could be applied in E-Commerce.

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Crawford, P. "Review: Intelligent Software Agents." Computer Bulletin 41, no. 3 (May 1, 1999): 31. http://dx.doi.org/10.1093/combul/41.3.31-c.

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Cross, Stuart R. "Agency, Contract and Intelligent Software Agents." International Review of Law, Computers & Technology 17, no. 2 (July 2003): 175–89. http://dx.doi.org/10.1080/1360086032000122556.

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SYCARA, KATIA, and DAJUN ZENG. "COORDINATION OF MULTIPLE INTELLIGENT SOFTWARE AGENTS." International Journal of Cooperative Information Systems 05, no. 02n03 (June 1996): 181–211. http://dx.doi.org/10.1142/s0218843096000087.

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We are investigating techniques for developing distributed and adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system architecture has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We have implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making, investment counseling, health care and electronic commerce.

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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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Vinita, Kumari Pal, Singh Sonali, Sinha Anshita, and Sohail Shekh Mohammad. "Medical chatbot using AI and NLP." i-manager’s Journal on Software Engineering 16, no. 3 (2022): 46. http://dx.doi.org/10.26634/jse.16.3.18551.

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The use of chatbots has grown rapidly across industries, including marketing, assistive systems, education, healthcare, cultural heritage, and entertainment. This paper discusses the incentives for using chatbots and explains how useful chatbots are in various contexts. As intelligent software and hardware, also known as intelligent agents, are developed and analyzed, Artificial Intelligence (AI) is becoming more and more integrated into daily lives. From manual labor to complex procedures, intelligent agents are capable of performing a wide range of tasks. One of the simplest and most common forms of intelligent human-computer interaction is the chatbot, which is a classic example of an artificial intelligence Human-Computer Interaction (HCI) system. A chatbot is described as "a computer program designed to simulate interaction with human users, particularly over the Internet." In addition to chatbots, it also called smart bots, interactive agents, digital assistants, and intelligent conversational objects. In the midst of the COVID-19 pandemic, going to the doctor is no longer an indulgence. A chatbot is a Natural Language Processing (NLP) based chatbot to help with basic medical questions. Only the best knowledge of a chatbot can be used to answer medical questions.

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Hongbing, Li, Meng Bo, and Chen Shifu. "An Agent-Based Approach for Constructing Software Systems of Virtual Simulation." International Journal of Virtual Reality 4, no. 4 (January 1, 2000): 97–109. http://dx.doi.org/10.20870/ijvr.2000.4.4.2659.

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The design and construction of virtual reality environments involve technologies such as computer graphics, image processing, pattern recognition, intelligent interface, artificial intelligence, voice recognition, network, parallel processing, and high-performance computing. Some researchers insist that object-oriented and agent-oriented technologies are fundamental for virtual reality system design. This paper applies artificial intelligence to the design of virtual reality systems. Agents are constructed by using object-oriented methods and a set of underlying computing models, such as neural networks, genetic algorithms, expert systems, and plan managers. Some object-oriented frameworks of these computing models are presented to illustrate this approach. The example of a spaceship game will illustrate interactions among environments, agents, and underlying computing models. The approach and reusable class library presented herein can be applied to various virtual reality environment simulations and intelligent applications.

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Weinbaum (Weaver), David, and Viktoras Veitas. "Open ended intelligence: the individuation of intelligent agents." Journal of Experimental & Theoretical Artificial Intelligence 29, no. 2 (May 24, 2016): 371–96. http://dx.doi.org/10.1080/0952813x.2016.1185748.

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Karatza, H. D. "Intelligent software agents' influence on media-based systems." IEEE Distributed Systems Online 5, no. 3 (March 2004): 1–3. http://dx.doi.org/10.1109/mdso.2004.1285881.

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Fougères, Alain-Jérôme, and Egon Ostrosi. "Intelligent agents for feature modelling in computer aided design." Journal of Computational Design and Engineering 5, no. 1 (November 6, 2017): 19–40. http://dx.doi.org/10.1016/j.jcde.2017.11.001.

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Abstract CAD modelling can be referred to as the process of generating an integrated multiple view model as a representation of multiple views of engineering design. In many situations, a change in the model of one view may conflict with the models of other views. In such situations, the model of some views needs to be adapted in order to make all models consistent. Thus, CAD models should be capable of adapting themselves to new situations. Recently, agent based technologies have been considered in order to increase both knowledge level and intelligence of real and virtual objects. The contribution of this paper consists in introducing the intelligent agents in intelligent CAD modelling. The proposed agents are elementary geometrical and topological objects. They incorporate the functions of observation, decision and action, and possess their own knowledge. Agents have the capacity of communication and inference based on the feature grammars. They are modelled as bio-dynamic objects that enjoy the properties of fusion, division and multiplication. Being aware of the context, the proposed agents interact to form potential regional transitory communities, called regions. Being aware of their belonging in a region, agents interact by generating virtual links (virtual extensions). These virtual links produce: (a) fusion of agents, (b) division of agents and c) multiplication of agents. The emerged agents interact with the other agents in a region to recognize each other and to form specific sub-communities, called intelligent features. From a CAD software development point of view, this paper advocates the idea of a new phase of CAD system development based on the agent-oriented programming (AOP) paradigm. Highlights This paper proposes the agent paradigm for intelligent CAD modelling. Second section presents the state of art. In the third section, using the linguistic hypothesis of product design, a feature modelling formalism is presented. Fourth section presents a formal model for agent modelling. In the fifth section, agents for feature generation and modelling are formalised and modelled. The sixth section presents the application of the method. Finally, in the last section, the conclusion and future developments are proposed.

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MASCARDI, VIVIANA, MAURIZIO MARTELLI, and LEON STERLING. "Logic-based specification languages for intelligent software agents." Theory and Practice of Logic Programming 4, no. 04 (June 7, 2004): 429–94. http://dx.doi.org/10.1017/s1471068404002029.

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Moise, Gabriela. "A Software System for Online Learning Applied in the Field of Computer Science." International Journal of Computers Communications & Control 2, no. 1 (January 1, 2007): 84. http://dx.doi.org/10.15837/ijccc.2007.1.2340.

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<p>The computer-assisted learning is a very modern study area, which can be applied to the learning process. The main objective of this paper is to present a software system for online learning based on the intelligent software agents technologies. The main ideas on which this paper is built are: to any person is associated a learning profile (the idea is based on the existence of multiple intelligences, defined by Gardner [3]); the pedagogical resources can be shaped through educational semantic networks or through conceptual maps; a flexible software system in computer assisted learning must be based on the intelligent agents’ technology. The system dedicated to computer-assisted learning must be adapted to the learning profile of each student.<br />The author presents a flexible online teaching software system, which learns to teach according to the learning profile of each student (the author defines this system in the PhD thesis and includes: intelligent agent structures, reward learning algorithms, algorithms to generate plans for an agent).<br />The application includes two agents: the supervising agent and the pedagogical agent, which determines the optimal pedagogical resources for teaching the course. The application has been designed in Microsoft Visual Studio 6.0 and uses Microsoft Agent Technology, which allows vocal recognition. Also, the Protéjé 3.0 software has been used, software that allows building ontology for computer assisted learning. The system has been experimented on the Graph Theory Course, taught at postuniversitary computer science courses, the results proving the necessity of defining a strategy for selecting the pedagogical resources presented to the students according to their learning profile.</p>

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HAGRAS, H., V. CALLAGHAN, and M. COLLEY. "Intelligent embedded agents." Information Sciences 171, no. 4 (May 13, 2005): 289–92. http://dx.doi.org/10.1016/j.ins.2004.09.006.

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Ng, Y. C., K. S. Tey, K. R. Subramanian, S. B. Tor, L. P. Khoo, and S. G. Lee. "An Intelligent, Multi-Agent Environment for Concurrent and Collaborative Configuration of Personal Computers." Concurrent Engineering 10, no. 2 (June 2002): 143–51. http://dx.doi.org/10.1177/1063293x02010002639.

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Although Concurrent and Collaborative Engineering (CCE) has enjoyed widespread acceptance in industry, many implementation problems remain. With the advent of more powerful artificial intelligence techniques, CCE can be further improved. This paper demonstrates how intelligent software agents may be deployed to facilitate concurrent, collaborative engineering. A system architecture, Java Agent Alive!, is presented as a multi-agent environment. A case study of configuring a personal computer (PC) from its processor, memory and hard disk drive is discussed to highlight the power of software agents in negotiating for the PC configuration with the best price and performance. A software agent is created and assigned to each of the PC components. These agents attend two levels of agent conferences, viz. the bidding conference and the PC component vendor's conference. At both conferences, each agent strives to offer components with the best performance and the lowest price. The agents were ascribed artificial intelligence through the Java Expert System Shell (JESS). At the end of the negotiations, five PC configurations were finalised that met the expectations of the user, who is informed of the outcome via e-mail. The strengths and limitations of the system architecture and the domain application of PC assembly, as well as means to enhance security, are also discussed. Some recommendations to further improve the limitations of Java Agent Alive! and the PC Assembly application are made.

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Farzaneh, Mandana, Iman Raeesi Vanani, and Babak Sohrabi. "Utilization of Intelligent Software Agent Features for Improving E-Learning Efforts." International Journal of Virtual and Personal Learning Environments 3, no. 1 (January 2012): 55–68. http://dx.doi.org/10.4018/ijvple.2012010104.

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E-learning is one of the most important learning approaches within which intelligent software agents can be efficiently used so as to automate and facilitate the process of learning. The aim of this paper is to illustrate a comprehensive categorization of intelligent software agent features, which is valuable for being deployed in the virtual world of learning and training. The characteristics of agents make them worthwhile for being used in the implementation and improvement of e-learning information systems, especially the web-based or web-enabled e-learning mechanisms. An extensive exploration of agent-related international papers has been conducted so as to cover a wide range of internationally accepted features as well as the classification of agents and their correspondent references. These agent characteristics enable the online learning mechanisms to act on behalf of the user in electronic learning and to respond to specific requirements in a more efficient and effective manner than the traditional learning systems. The implementation of agents according to the most useful features helps teachers transfer the related knowledge easier, faster, and more directed towards the targeted audience of the e-learning system for a better and deeper learning quality.

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PRAKKEN, HENRY. "Formal systems for persuasion dialogue." Knowledge Engineering Review 21, no. 2 (June 2006): 163–88. http://dx.doi.org/10.1017/s0269888906000865.

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This article reviews formal systems that regulate persuasion dialogues. In such dialogues two or more participants aim to resolve a difference of opinion, each trying to persuade the other participants to adopt their point of view. Systems for persuasion dialogue have found application in various fields of computer science, such as non-monotonic logic, artificial intelligence and law, multi-agent systems, intelligent tutoring and computer-supported collaborative argumentation. Taking a game-theoretic view on dialogue systems, this review proposes a formal specification of the main elements of dialogue systems for persuasion and then uses it to critically review some of the main formal systems for persuasion. The focus of this review will be on regulating the interaction between agents rather than on the design and behaviour of individual agents within a dialogue.

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Barfield, Woodrow. "Issues of Law for Software Agents within Virtual Environments." Presence: Teleoperators and Virtual Environments 14, no. 6 (December 2005): 741–48. http://dx.doi.org/10.1162/105474605775196607.

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A software agent is a computer program that operates within computing environments. The owners of software agents may instruct their agents to roam the networks, access desired information by exchanging data with other agents or people, and handle business and personal transactions. As the interactions between software agents and humans become more frequent, it is relevant to ask whether there are any issues of law that may guide their interactions and conduct. For example, as the agents become more intelligent and autonomous, who will be responsible for the mistakes that software agents make? Will software agents be allowed to contract with humans and with each other, and if so will such contracts be enforceable? And, will software agents have standing to sue and be sued? While there are a host of legal issues associated with software agents operating within virtual environments, the main issue addressed in this paper is whether software agents should be granted the legal rights associated with personhood. After discussing basic characteristics of software agents, and personhood in general, the paper concludes by outlining three possible scenarios that could represent the legal status of software agents in the future; these include the current status quo of property, the status of an indentured servant, and the status and associated rights of legal personhood.

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Hoey, Jesse, Tobias Schröder, Jonathan Morgan, Kimberly B. Rogers, Deepak Rishi, and Meiyappan Nagappan. "Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale." Small Group Research 49, no. 6 (October 3, 2018): 647–83. http://dx.doi.org/10.1177/1046496418802362.

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Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.

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Schlimmer, J. C., and L. A. Hermens. "Software Agents: Completing Patterns and Constructing User Interfaces." Journal of Artificial Intelligence Research 1 (November 1, 1993): 61–89. http://dx.doi.org/10.1613/jair.25.

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To support the goal of allowing users to record and retrieve information, this paper describes an interactive note-taking system for pen-based computers with two distinctive features. First, it actively predicts what the user is going to write. Second, it automatically constructs a custom, button-box user interface on request. The system is an example of a learning-apprentice software- agent. A machine learning component characterizes the syntax and semantics of the user's information. A performance system uses this learned information to generate completion strings and construct a user interface. Description of Online Appendix: People like to record information. Doing this on paper is initially efficient, but lacks flexibility. Recording information on a computer is less efficient but more powerful. In our new note taking softwre, the user records information directly on a computer. Behind the interface, an agent acts for the user. To help, it provides defaults and constructs a custom user interface. The demonstration is a QuickTime movie of the note taking agent in action. The file is a binhexed self-extracting archive. Macintosh utilities for binhex are available from mac.archive.umich.edu. QuickTime is available from ftp.apple.com in the dts/mac/sys.soft/quicktime.

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Fortino, Giancarlo, and Antonio Guerrieri. "Decentralized management of building indoors through embedded software agents." Computer Science and Information Systems 9, no. 3 (2012): 1331–59. http://dx.doi.org/10.2298/csis120101030f.

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In order to support personalized people comfort and building energy efficiency as well as safety, emergency, and context-aware information exchange scenarios, next-generation buildings will be smart. In this paper we propose an agent-oriented decentralized and embedded architecture based on wireless sensor and actuator networks (WSANs) for enabling efficient and effective management of buildings. The main objective of the proposed architecture is to fully support distributed and coordinated sensing and actuation operations. The building management architecture is implemented at the WSAN side through MAPS (Mobile Agent Platform for Sun SPOTs), an agent-based framework for programming WSN applications based on the Sun SPOT sensor platform, and at the base station side through an OSGi-based application. The proposed agent-oriented architecture is demonstrated in a simple yet effective operating scenario related to monitoring workstation usage in computer laboratories/offices. The high modularity of the proposed architecture allows for easy adaptation of higher-level application-specific agents that can therefore exploit the architecture to implement intelligent building management policies.

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Kieltyka, Leszek. "Knowledge Management in Multimedia Communication Using Software Agents." European Conference on Knowledge Management 24, no. 1 (September 5, 2023): 666–72. http://dx.doi.org/10.34190/eckm.24.1.1480.

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This paper presents the problem of knowledge management related to software agents and agent systems (i.e. systems composed of communicating and cooperating software agents). The author presents basic theoretical issues, but the main focus is on the areas of application of software agents in multimedia communication - both existing and prospective applications. Particular attention has been paid to the applications of software agents for: supporting customer service processes through the Contact Centre system; supporting the distance learning system; creating virtual assistants in the form of conversation bots; searching for information in the Semantic Web, and assisting in setting up a video conference call. Software agents are often referred to as 'intelligent' - due to their intelligent behaviour. At their core, they involve: perception, reasoning, learning, communication and operation in complex environments, using all multimedia techniques and procedures. Software agents and agent systems, as presented in this article, represent a new approach to the analysis, design and implementation of complex (usually decentralised and distributed) computer systems, offering a whole range of concepts, techniques and tools to significantly improve the software development process. The ability of software agents to make autonomous decisions have been a subject of considerations. Among other things, they are based on the example of cognitive sciences, which deal with modelling real processes of information processing in humans and building programmes that imitate these processes. Research on the common understanding of shared knowledge and knowledge management in agent-based systems focuses on ontologies, understood as a description of the relationship between the way knowledge is represented and the concept represented by that knowledge. In contrast, research on knowledge sharing has focused on communication languages and their associated - interaction protocol and message transport protocol.

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Yampolskiy, Roman V. "Utility function security in artificially intelligent agents." Journal of Experimental & Theoretical Artificial Intelligence 26, no. 3 (April 8, 2014): 373–89. http://dx.doi.org/10.1080/0952813x.2014.895114.

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Torres, Daniel. "On virtual environments and agents in next-generation computer games." Knowledge Engineering Review 23, no. 4 (December 2008): 389–97. http://dx.doi.org/10.1017/s0269888908000040.

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AbstractThis paper holds the material presented at our keynote at the Intelligent Virtual Environments and Virtual Agents Conference, where we analyzed future trends in virtual agent and environment technologies, as applied to the fields of videogames and interactive multimedia. We begin by observing current tendencies in game development projects, both in the market and in the academia, outlining their respective strengths and weaknesses. We follow by describing the aspects of game development that will come to shape the next generation of games, and discuss the requirements for academic projects to achieve the level of quality required to be competitive in this field. In the rest of the paper we analyze the roles, importance, challenges, and research opportunities that a new generation of virtual characters and environments will bring to the world of interactive entertainment.

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Adomavicius, Gediminas, Alok Gupta, and Dmitry Zhdanov. "Designing Intelligent Software Agents for Auctions with Limited Information Feedback." Information Systems Research 20, no. 4 (December 2009): 507–26. http://dx.doi.org/10.1287/isre.1080.0172.

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Caro, Manuel Fernando, Darsana P. Josyula, Dalia Patricia Madera, Catriona M. Kennedy, and Adán A. Gómez. "The CARINA Metacognitive Architecture." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 4 (October 2019): 71–90. http://dx.doi.org/10.4018/ijcini.2019100104.

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Metacognition has been used in artificial intelligence to increase the level of autonomy of intelligent systems. However, the design of systems with metacognitive capabilities is a difficult task due to the number and complexity of processes involved. The main objective of this article is to introduce a novel metacognitive architecture for monitoring and control of reasoning failures in artificial intelligent agents. CARINA metacognitive architecture is based on precise definitions of structural and functional elements of metacognition as defined in the MISM meta-model. CARINA can be used to implement real-world cognitive agents with the capability for introspective monitoring and meta-level control. Introspective monitoring detects reasoning failure (for example, when expectation is violated). Metacognitive control selects strategies to recover from failures. The article demonstrates a CARINA implementation of reasoning failure detection and recovery in an intelligent tutoring system called FUNPRO.

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Jávor, András, and Attila Fűr. "Simulation on the Web with distributed models and intelligent agents." SIMULATION 88, no. 9 (July 2, 2012): 1080–92. http://dx.doi.org/10.1177/0037549712450359.

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Simulation is aimed very often to solve problems of great complexity requiring – beyond using the advanced simulation software tools – platforms that enable the implementation of such software systems. In recent years the concept of cloud computing has emerged and is being applied more and more widely for solving such problems. This paper, beyond delineating the main trends of the development of distributed simulation over a grid, especially over the Internet through Web-based applications, highlights the concepts of service-based simulation system approach. This concept gives the possibility of implementing Web- or cloud agents and other ASP system compliant simulation services based on simulation standards. As a sample application, Fuzzy Web Service is demonstrated as a part of CASSANDRA 4.0 (Cognizant Adaptive Simulation System for Applications in Numerous Different Relevant Areas) that is developed by the McLeod Institute of Simulation Sciences Hungarian Center.

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Cao, Junwei, Daniel P. Spooner, Stephen A. Jarvis, and Graham R. Nudd. "Grid load balancing using intelligent agents." Future Generation Computer Systems 21, no. 1 (January 2005): 135–49. http://dx.doi.org/10.1016/j.future.2004.09.032.

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Sklar, Elizabeth, and Debbie Richards. "Agent-based systems for human learners." Knowledge Engineering Review 25, no. 2 (June 2010): 111–35. http://dx.doi.org/10.1017/s0269888910000044.

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AbstractApplying intelligent agent technologies to support human learning activities has been the subject of recent work that reaches across computer science and education disciplines. This article discusses agent-based approaches that have been designed to address a range of pedagogical and/or curricular tasks. Three types of agents are identified in the literature:pedagogical agents,peer-learning agents, anddemonstrating agents. Features of each type are considered, as well as the systems in which these agents are incorporated, examining common and divergent goals, system and agent architectures, and evaluation methodologies. Open issues are highlighted, and future directions for this burgeoning interdisciplinary field are suggested.

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Mateos, Cristian, Alejandro Zunino, and Marcelo Campo. "Integrating Intelligent Mobile Agents with Web Services." International Journal of Web Services Research 2, no. 2 (April 2005): 85–103. http://dx.doi.org/10.4018/jwsr.2005040105.

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Herrero, Pilar, and Angélica de Antonio. "Intelligent virtual agents keeping watch in the battlefield." Virtual Reality 8, no. 3 (February 23, 2005): 185–93. http://dx.doi.org/10.1007/s10055-004-0148-7.

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Бождай, Александр, Aleksandr Bozhday, Александр Бершадский, Aleksandr Bershadskiy, Вардан Мкртчан, and Vardan Mkrtchan. "Knowledge management methods in next-generation E/U Learning systems." Russian Journal of Management 3, no. 4 (August 31, 2015): 352–60. http://dx.doi.org/10.12737/13099.

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The article discusses the methods of knowledge management for E/U-Learning systems, which will provide the opportunities for the construction of a unified all-pervasive electronic educational environment. Authors analyze the development of a new phase of e-learning (E-Learning 3.0) in which will play an important role distributed computer systems, cloud computing, mobile personal devices and wireless networks, artificial intelligence and virtual reality tools. Today, there is a fundamental scientific problem of creating a unified interstate intellectual environment for e-education services. This environment should include heterogeneous forms of knowledge representation, interstate standards and forms of learning, international faculty and student teams. The article suggests a possible knowledge management methods and the approach to the construction of an all-pervading intellectual environment for e-learning services. The proposed approach is based on the integration of multiple technologies, such as: service-oriented design, engineering of DSPL (Dynamic Software Product Lines), wireless telecommunications, interaction of intelligent e-learning agents. Structural basis of this approach is interconnected pair of intelligent software agents: student’s and teacher’s software agents. These agents are able to adapt itself: to the current level of student’s knowledge; to the available software, hardware and network equipment; to the current demands of the environment (e.g. labor markets); to the modern educational standards. As a basis of software agents self-adaptation is a mathematical variability model, including the three basic features hierarchy: educational content, interface, software and technical support. Application of such variability model does not require recompilation of the source code (to make changes in the agents properties) and allows to organize a continuous process of e-learning and significantly increase the life cycle of the entire system of distance learning. Moreover, the authors propose a service-oriented scheme for knowledge flows management. The article will be useful for experts in e-learning, developers of service-oriented systems and for managers in the field of socio-economic systems.

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Ganzha, Maria. "Book Review: E-learning with Intelligent Agents [review of "Designing Distributed Learning Environments with Intelligent Software Agents" (Lin, F.O., ed.; 2004)]." IEEE Distributed Systems Online 9, no. 2 (February 2008): 4. http://dx.doi.org/10.1109/mdso.2008.6.

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33

Yager, Ronald R. "Intelligent agents for World Wide Web advertising decisions." International Journal of Intelligent Systems 12, no. 5 (May 1997): 379–90. http://dx.doi.org/10.1002/(sici)1098-111x(199705)12:5<379::aid-int2>3.0.co;2-k.

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34

Sankaranarayanan, Suresh, and Subramaniam Ganesan. "Applications of Intelligent Agents in Health Sector-A Review." International Journal of E-Health and Medical Communications 7, no. 1 (January 2016): 1–30. http://dx.doi.org/10.4018/ijehmc.2016010101.

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Health sector is a growing field and health conscience has been a major concern for everyone. Particularly in the health care sector, Information and communication Technology, enabled devices like Smart phones, Tablet PC, Sensors, RFID etc are predominantly used for ease of access and use towards decision processing. So looking into expectations of making devices smarter and intelligent enough for health care application, there has been a growing interest in the use of Software agents in health care application. This Survey paper predominantly concentrates on showing the usage and application of intelligent agent technology in health care sector pertaining to hospital search & appointment, patient health monitoring and mobile health record, with a thorough literature review and appropriate screenshots necessary.

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Haynes, Steven R., Mark A. Cohen, and Frank E. Ritter. "Designs for explaining intelligent agents." International Journal of Human-Computer Studies 67, no. 1 (January 2009): 90–110. http://dx.doi.org/10.1016/j.ijhcs.2008.09.008.

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36

Melekhin, V. B., and M. V. Khachumov. "Instrumental Means for Managing the Rational Behavior of Self-Organizing Autonomous Intelligent Agents." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 4 (April 5, 2021): 171–80. http://dx.doi.org/10.17587/mau.22.171-180.

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We formulate the basic principles of constructing a sign-signal control for the expedient behavior of autonomous intelligent agents in a priori undescribed conditions of a problematic environment. We clarify the concept of a self-organizing autonomous intelligent agent as a system capable of automatic goal-setting when a certain type of conditional and unconditional signal — signs appears in a problem environment. The procedures for planning the expedient behavior of autonomous intelligent agents have been developed, that imitate trial actions under uncertainty in the process of studying the regularities of transforming situations in a problem environment, which allows avoiding environmental changes in the process of self-learning that are not related to the achievement of a given goal. Boundary estimates of the proposed procedures complexity for planning expedient behavior are determined, confirming the possibility of their effective implementation on the on-board computer of the automatic control system for the expedient activity of autonomous intelligent agents. We carry out an imitation on a personal computer of the proposed procedures for planning purposeful behavior, confirming the effectiveness of their use to build intelligent problem solvers for autonomous intelligent agents in order to endow them with the ability to adapt to a priori undescribed operating conditions. The main types of connections between various conditional and unconditional signal — signs of a problem environment are structured, which allows autonomous intelligent agents to adapt to complex a priori undescribed and unstable conditions of functioning.

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Ivanovic, Mirjana, Zoran Budimac, Milos Radovanovic, Vladimir Kurbalija, Weihui Dai, Costin Bădică, Mihaela Colhon, Srdjan Ninkovic, and Dejan Mitrovic. "Emotional agents - state of the art and applications." Computer Science and Information Systems 12, no. 4 (2015): 1121–48. http://dx.doi.org/10.2298/csis141026047i.

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last decade, intensive research on emotional intelligence has advanced significantly from its theoretical basis, analytical studies and processing technology to exploratory applications in a wide range of real-life domains. This paper brings new insights in the field of emotional, intelligent software agents. The first part is devoted to an overview of the state-of-the-art in emotional intelligence research with emphasis on emotional agents. A wide range of applications in different areas like modeling emotional agents, aspects of learning in emotional environments, interactive emotional systems and so on are presented. After that we suggest a systematic order of research steps with the idea of proposing an adequate framework for several possible real-life applications of emotional agents. We recognize that it is necessary to apply specific methods for dynamic data analysis in order to identify and discover new knowledge from available emotional information and data sets. The last part of the paper discusses research activities for designing an agent-based architecture, in which agents are capable of reasoning about and displaying some kind of emotions based on emotions detected in human speech, as well as online documents.

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Sbodio, Marco Luca, David Martin, and Claude Moulin. "Discovering Semantic Web services using SPARQL and intelligent agents." Journal of Web Semantics 8, no. 4 (November 2010): 310–28. http://dx.doi.org/10.1016/j.websem.2010.05.002.

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39

Selva, Deepaa, Balakrishnan Nagaraj, Danil Pelusi, Rajendran Arunkumar, and Ajay Nair. "Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms." Algorithms 14, no. 8 (July 26, 2021): 224. http://dx.doi.org/10.3390/a14080224.

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Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.

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BODANESE, ELIANE L., and LAURIE G. CUTHBERT. "APPLYING INTELLIGENT SOFTWARE AGENTS IN A DISTRIBUTED CHANNEL ALLOCATION SCHEME FOR CELLULAR NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 08 (December 2002): 1021–39. http://dx.doi.org/10.1142/s0218001402002131.

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As the demand for mobile services has increased, the need for an efficient allocation of channels is essential to ensure good performance, given the limited spectrum available. Techniques for increasing flexibility in radio resource acquisition are needed to handle the heterogeneity of services and bit rates to be supported in the forthcoming generations of mobile communications. To improve the performance and efficiency of the channel allocation, we propose the use of a particular agent architecture that allows base stations to be more flexible and intelligent, including planning to attempt to balance the load in advance of reactive requests. The simulation results prove that the use of intelligent agents controlling the allocation of channels is feasible and the agent negotiation is an important feature of the system in order to improve perceived quality of service and to improve the load balancing of the traffic.

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HAYES-ROTH, BARBARA. "A domain-specific software architecture for a class of intelligent patient monitoring agents." Journal of Experimental & Theoretical Artificial Intelligence 8, no. 2 (April 1996): 149–71. http://dx.doi.org/10.1080/095281396147438.

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42

Elofson, G. "Intelligent agents extend knowledge-based systems feasibility." IBM Systems Journal 34, no. 1 (1995): 78–95. http://dx.doi.org/10.1147/sj.341.0078.

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43

Ziemke, Tom. "Adaptive Behavior in Autonomous Agents." Presence: Teleoperators and Virtual Environments 7, no. 6 (December 1998): 564–87. http://dx.doi.org/10.1162/105474698565947.

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.

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Keeble, R. J., and R. D. Macredie. "Assistant agents for the world wide web intelligent interface design challenges." Interacting with Computers 12, no. 4 (February 2000): 357–81. http://dx.doi.org/10.1016/s0953-5438(99)00004-1.

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45

Wolgast, Thomas, Nils Wenninghoff, Stephan Balduin, Eric Veith, Bastian Fraune, Torben Woltjen, and Astrid Nieße. "ANALYSE — Learning to attack cyber–physical energy systems with intelligent agents." SoftwareX 23 (July 2023): 101484. http://dx.doi.org/10.1016/j.softx.2023.101484.

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King, Roger L., Samuel H. Russ, Aric B. Lambert, and Donna S. Reese. "An artificial immune system model for intelligent agents." Future Generation Computer Systems 17, no. 4 (January 2001): 335–43. http://dx.doi.org/10.1016/s0167-739x(99)00115-6.

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47

Persson, P., J. Laaksolahti, and P. Lonnqvist. "Understanding socially intelligent agents - a multilayered phenomenon." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 31, no. 5 (2001): 349–60. http://dx.doi.org/10.1109/3468.952710.

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Zafar Jafarov, Konstantin Barabanov, Zafar Jafarov, Konstantin Barabanov. "PURPOSE AND ADVANTAGES OF CHATBOTS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 42, no. 07 (May 25, 2024): 114–22. http://dx.doi.org/10.36962/pahtei42072024-13.

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Digital technologies, in particular chatbots, are becoming a key element of interaction between companies (brands) and consumers, performing various functions, replacing a person and imitating his behavior. They find new functional areas related to the current activities of the organization, acquire new competencies that affect the level of market flexibility of the business and its operational efficiency. Chatbots can guide shoppers through transactional steps, saving them cognitive effort and time, and become an asset that differentiates a company or brand from its competitors. The authors believe that the personalized diversification of chatbots across economic sectors and market sectors will increase the level of maturity of companies and brands in the digital environment, aimed at public recognition and universal socialization. Machine learning gives customer service chatbots the ability to detect emotions and also emotionally engage with customers just like human agents do. The development and analysis of intelligent software and hardware, called intelligent agents, are becoming more and more integrated into everyday life. Intelligent agents perform a variety of tasks, from simple to complex operations. Chatbot, which is a typical example of an artificial intelligence system; communicates with users through messaging apps, websites, mobile apps or phone. Chatbots are one of the simplest and most common examples of human-computer interaction (HCI). In chatbot software, the process begins with understanding the offer sent by the user. The sentence submitted by the user is analyzed using natural language processing (NLP) to understand what the user means. Thus, as a result of analyzing the proposal, artificial intelligence makes a decision like a person and transmits to the user the most appropriate answer to the question. Some chatbots powered by machine learning learn more and become smarter as they interact with users. Below are some chatbot software concepts: Chatbots, which are one of the tools that can be preferred to quickly communicate with users, save time. Chatbots, which are becoming increasingly popular today, are coming to the fore in business activities. In this context, the use of chatbots in marketing and customer relations is growing every day. With the help of chatbots, it becomes much easier to reach a large customer base. Keywords: dialog box, artificial intelligence, communications, consumers, chatbots.

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Benoudifa, Ouafae, Abderrahim Ait Wakrime, and Redouane Benaini. "Autonomous solution for Controller Placement Problem of Software-Defined Networking using MuZero based intelligent agents." Journal of King Saud University - Computer and Information Sciences 35, no. 10 (December 2023): 101842. http://dx.doi.org/10.1016/j.jksuci.2023.101842.

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50

TURNER, ROY M. "Context-mediated behavior for intelligent agents." International Journal of Human-Computer Studies 48, no. 3 (March 1998): 307–30. http://dx.doi.org/10.1006/ijhc.1997.0173.

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