Relevant bibliographies by topics / Intelligent agent

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent agent'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Intelligent agent"

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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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Dissertations / Theses on the topic "Intelligent agent"

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Nickolaenko, A. V., and D. O. Marchenko. "Affective intelligent driving agent (AIDA)." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31087.

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It’s impossible to imagine modern world without using motor cars. AIDA aims to change the way we interact with our car. MIT(Massachusetts Institute of Technology) researchers and designers are developing the Affective Intelligent Driving Agent (AIDA) - a new in-car personal robot that brings a revolution to life of all motor-cars’ users. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/31087
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Tantrairatn, Suradet. "Intelligent control agent for autonomous UAS." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13543/.

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A self reconfiguring autopilot system is presented, which is based on a rational agent framework that integrates decision making with abstractions of sensing and actions for next generation unmanned aerial vehicles. The objective of the new intelligent control system is to provide advanced capabilities of self-tuning control for a new UAS airframe or adaptation for an old UAS in the presence of failures in adverse flight conditions. High-level system performance is achieved through on-board dynamical monitoring and estimation associated with controller switching and tuning by the agent. The agent can handle an untuned autopilot or retune the autopilot when dynamical changes occur due to aerodynamic and on-board system changes. The system integrates dynamical modelling, hybrid adaptive control, model validation, flight condition diagnosis, control performance evaluation through software agent development. An important feature of the agent is its abstractions from real-time measurements and also its abstractions from model based on-board simulation. The agent, while tuning and supervising the autopilot, also performs real-time evaluations on the effects of its actions.
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Rahwan, Iyad. "Interest-based negotiation in multi-agent systems." Connect to thesis, 2004. http://repository.unimelb.edu.au/10187/2885.

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Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)
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Hu, Yuebing, and 胡跃冰. "Consensus control of multi-agent systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4658657X.

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LISNYAK, TATYANA. "AN AGENT-BASED SYSTEM FOR INTELLIGENT INTERNET SHOPPING." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1026130092.

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Wang, Xin. "A fuzzy logic based intelligent negotiation agent." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27194.

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With the evolution of electronic commerce (eCommerce) on the web and the rise of interests in intelligence of software agents, automated negotiation is becoming an increasingly popular method for an eCommerce system to be efficient; however, negotiation, which takes place in transaction, is complicated, time-consuming and costly for participants to reach an agreement. This thesis presents a model of an intelligent negotiation agent based on fuzzy logic methodology in order to alleviate the complexity of negotiation. The proposed negotiation agent model is particularly suitable to open environments, such as the Internet. The conventional methods, such as game theory, are incapable of handling an open environment where the information is sparse and full of uncertainty, while the fuzzy approaches are suitable to elegantly deal with this problem. The fuzzy logic based intelligent negotiation agent, presented in this thesis, is able to interact autonomously and consequently save human labor in negotiations. The aim of modeling a negotiation agent is to reach mutual agreement efficiently and intelligently. The negotiation agent is able to negotiate with other such agents, over various sets of issues, on behalf of the real-world parties they represent, i.e. it can handle multi-issue negotiation. The reasoning model of the negotiation agent has been implemented partially by using c# based on Microsoft .NET. The reliability and the flexibility of the reasoning model are finally evaluated. The results show that performance of the proposed agent model is acceptable for negotiation parties to achieve mutual benefits.
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Abdullah, Bashar. "Intelligent multi-agent-based virtual gauges (IMAGES)." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ38731.pdf.

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Jing, Yanguo. "A model based intelligent interface agent architecture." Thesis, Heriot-Watt University, 2004. http://hdl.handle.net/10399/328.

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Rabelo, Mendizabal Luis C. (Luis Carlos) 1960. "What intelligent agent is smarter? : a comparison." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/91726.

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Mora, Randall P., and Jerry L. Hill. "Service-Based Approach for Intelligent Agent Frameworks." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595661.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
This paper describes a service-based Intelligent Agent (IA) approach for machine learning and data mining of distributed heterogeneous data streams. We focus on an open architecture framework that enables the programmer/analyst to build an IA suite for mining, examining and evaluating heterogeneous data for semantic representations, while iteratively building the probabilistic model in real-time to improve predictability. The Framework facilitates model development and evaluation while delivering the capability to tune machine learning algorithms and models to deliver increasingly favorable scores prior to production deployment. The IA Framework focuses on open standard interoperability, simplifying integration into existing environments.
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Books on the topic "Intelligent agent"

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1967-, Alonso Eduardo, Kudenko Daniel 1968-, and Kazakov Dimitar 1967-, eds. Adaptive agents and multi-agent systems, adaptation and multi-agent learning. Berlin: Springer, 2003.

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1968-, Kudenko Daniel, Kazakov Dimitar 1967-, Alonso Eduardo 1967-, and LINK (Online service), eds. Adaptive agents and multi-agent systems III: Adaptation and multi-agent learning. Berlin: Springer, 2005.

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Bui, The Duy, Tuong Vinh Ho, and Quang Thuy Ha, eds. Intelligent Agents and Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6.

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Kuwabara, Kazuhiro, and Jaeho Lee, eds. Intelligent Agents and Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45680-5.

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Barley, Michael Wayne, and Nik Kasabov, eds. Intelligent Agents and Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b107183.

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Lee, Jaeho, and Mike Barley, eds. Intelligent Agents and Multi-Agent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b94219.

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Intelligent agent software engineering. Hershey, PA: Idea Group Pub., 2003.

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Padgham, Lin. Developing Intelligent Agent Systems. New York: John Wiley & Sons, Ltd., 2005.

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Duarte, Bouça, and Gafagnão Amaro, eds. Agent-based computing. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Wooldridge, Michael, Jörg P. Müller, and Milind Tambe, eds. Intelligent Agents II Agent Theories, Architectures, and Languages. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-60805-2.

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Book chapters on the topic "Intelligent agent"

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Jennings, N. R., and M. Wooldridge. "Applications of Intelligent Agents." In Agent Technology, 3–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03678-5_1.

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Weihmayer, R., and H. Velthuijsen. "Intelligent Agents in Telecommunications." In Agent Technology, 203–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03678-5_11.

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Choi, Jonghwa, Dongkyoo shin, and Dongil Shin. "Intelligent Game Agent Based Physics Engine for Intelligent Non Player Characters." In Agent Computing and Multi-Agent Systems, 444–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802372_42.

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Sycara, K. P., D. Zeng, and K. Decker. "Intelligent Agents in Portfolio Management." In Agent Technology, 267–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03678-5_14.

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O’Hare, Gregory M. P., Brian R. Duffy, Bianca Schön, Alan N. Martin, and John F. Bradley. "Agent Chameleons: Virtual Agents Real Intelligence." In Intelligent Virtual Agents, 218–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39396-2_37.

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Radecký, Michal, Petr Gajdoš, and Radoslav Fasuga. "Agent Behavior Diagram for Intelligent Agents." In Networked Digital Technologies, 333–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14306-9_34.

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Janca, P. C., and D. Gilbert. "Practical Design of Intelligent Agent Systems." In Agent Technology, 73–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03678-5_4.

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Duc, Nguyen Tuan, and Ikuo Takeuchi. "Abstraction of Agent Cooperation in Agent Oriented Programming Language." In Intelligent Agents and Multi-Agent Systems, 307–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_34.

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Chen, Jessie Y. C. "Agent Transparency." In Smart and Intelligent Systems, 87–99. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003215349-4.

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Shah, Nazaraf, Edward Tsang, Yossi Borenstein, Raphael Dorne, Anne Liret, and Chris Voudouris. "Intelligent Agent Based Workforce Empowerment." In Agent and Multi-Agent Systems: Technologies and Applications, 163–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01665-3_17.

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Conference papers on the topic "Intelligent agent"

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Beuker, Tatjana, and Tina Miochand Mark A. Neerincx. "Team Design Patterns for Participatory Development of First Response Human-Agent Teaming." In Human Systems Engineering and Design (IHSED 2021) Future Trends and Applications. AHFE International, 2021. http://dx.doi.org/10.54941/ahfe1001152.

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First responders (FRs) work in complex and dangerous environments in which information is often uncertain and incomplete. Advancements in artificial intelligence technology pose great potential for supporting FRs throughout dynamic mission developments. We investigated how teamwork between FRs and an intelligent agent should be designed to facilitate adequate decision-making. For this purpose, three Team Design Patterns (TDPs) were created, each assigning different roles and responsibilities to the intelligent agents and FRs. The collaboration was evaluated by presenting two scenarios to FRs in which they had to handle an incident in simulated collaboration with the agents. The results show that preference and acceptance varied across FRs and different decisions and point towards recommending a design solution in which the intelligent agent can adapt its collaboration style to different FRs and decisions it is assisting with.
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Liu, Xinhua, Weida Wang, Wenjian Liu, and Yue Xing. "Development of a Technique Preparation Integration System With Agent Technology." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34142.

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This study intends to propose an intelligent system with agent technology in order to realize integration and cooperation of technique preparation process. The agent-based system framework, in which various intelligent agents worked together to perform technique preparation tasks in an autonomous and collaborative way, is put forward. The system consists of three categories of agents and functional definition of each intelligent agent is presented. Moreover, agents communication mechanism and cooperation sequence diagram are proposed. Finally, an intelligent algorithm based on fuzzy comprehensive evaluation is designed to resolve competition conflicts among the agents.
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Bhaskar, Kalyani, Asif Mansooor, Kumar R. P. Anand, and A. Ramani. "Intelligent mail delivery robot." In 2011 2nd International Conference on Intelligent Agent & Multi-Agent Systems. IEEE, 2011. http://dx.doi.org/10.1109/iama.2011.6048994.

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Liu, Jiming, and Ning Zhong. "Intelligent Agent Technology." In Proceedings of the 1st Asia-Pacific Conference on Intelligent Agent Technology (IAT '99). WORLD SCIENTIFIC, 1999. http://dx.doi.org/10.1142/9789814527149.

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Zhang, Xutang, Xinhua Liu, and Gaoliang Peng. "Development of a Multi-Project Production Planning and Scheduling System With Agent Technology." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-50039.

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This study intends to propose an intelligent system with agent technology in order to realize integration and cooperation of multi-project production planning and scheduling process. The agent-based system framework, in which various intelligent agents worked together to perform multi-project production planning and scheduling tasks in an autonomous and collaborative way, is put forward. The system consists of three categories of agents and functional definition of each intelligent agent is presented. Moreover, agents communication mechanism and cooperation sequence diagram are proposed. Furthermore, an intelligent algorithm based on fuzzy comprehensive evaluation is designed to resolve competition conflicts among the agents. Finally, an experiment example was illustrated and the algorithm was demonstrated feasible and efficient.
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Facchini, Sante Dino. "Decentralized Autonomous Organizations and Multi-agent Systems for Artificial Intelligence Applications and Data Analysis." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/828.

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The Ph.D research project aims to explore the potential of the Decentralized Autonomous Organization paradigm in conjunction with classic software architectures for Artificial Intelligence applications. The intended goal is to investigate and formalize a possible integration path between Multi-agent System architectures and Decentralized Autonomous Organizations. Starting from the Foundation for Intelligent Physical Agents standards, we will extend basic primitives to integrate Multi-agent Systems on Distributed Ledger Technology networks. Possible deployment of services and applications in the Internet-of-Things, Artificial Intelligence and Distributed Machine Learning areas will be tested. Application of Data Analysis techniques on datasets built on such a framework will be also addressed.
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Cruz-Piris, Luis, Diego Rivera, Ivan Marsa-Maestre, Enrique De la Hoz, and Susel Fernandez. "Intelligent Traffic Light Management using Multi-Behavioral Agents." In XIII Jornadas de Ingenieria Telematica - JITEL2017. Valencia: Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/jitel2017.2017.6494.

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One of the biggest challenges in modern societies is to solve vehicular traffic problems. In this scenario, our proposal is to use a Multi-Agent Systems (MAS) composed of three types of agent: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. This third type of agent is able to change its behaviour between what we have called a selfish mode (the agent will try to influence the other neighbour agents of its type to achieve its goal) or an altruistic mode (the agent will take into consideration the other neighbour selfish agents indications). To validate our solution, we have developed a MAS emulator which communicates with the Simulation of Urban MObility (SUMO) traffic simulator using the Traci tool to realize the experiments in a realistic environment. The obtained results show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration.
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Vinoth, M. S., Potharaju S. R. P. Saradhi, and Potharaju S. R. Aditya. "Intelligent guided missile." In Multi-Agent Systems (IAMA 2009). IEEE, 2009. http://dx.doi.org/10.1109/iama.2009.5228041.

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Acay, Daghan L., Gil Tidhar, and Liz Sonenberg. "Extending Agent Capabilities: Tools vs. Agents." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.247.

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Warwick, Kevin, and Huma Shah. "Intelligent Agents - Conversations from Human-agent Imitation Games." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005176702610268.

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Reports on the topic "Intelligent agent"

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Kramer, Mitch. VirtuOz Intelligent Virtual Agent. Boston, MA: Patricia Seybold Group, July 2012. http://dx.doi.org/10.1571/pr07-26-12cc.

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Hofmann, Martin. Intelligent Interoperable Agent Toolkit (I2AT). Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada430842.

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Deutsch, Stephen E. Intelligent Agent Feasibility Study. Volume 1: Agent-based System Technology. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada359612.

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Esterline, Albert. Benchmark Intelligent Agent Systems for Distributed Battle Tracking. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada501038.

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Andrade, Sylvio F., Neil C. Rowe, Donald P. Gaver, and Patricia A. Jacobs. Analysis of Shipboard Firefighting-Team Efficiency Using Intelligent-Agent Simulation. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada458054.

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How, Jonathan P., Emilio Frazzoli, and Nicholas Roy. ICPL: Intelligent Cooperative Planning and Learning for Multi-agent Systems. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada565746.

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SMATHERS, DOUGLAS C., and STEVEN Y. GOLDSMITH. Agent Concept for Intelligent Distributed Coordination in the Electric Power Grid. Office of Scientific and Technical Information (OSTI), March 2001. http://dx.doi.org/10.2172/780280.

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Chaiken, Scott R., Linda R. Elliott, Mathieu E. Dalrymple, Michael D. Coovert, and Dawn Riddle. Weapons Director Intelligent Agent-Assist Task: Procedure and Findings for a Validation Study. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada406635.

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Finin, Timothy, James Mayfield, and Benjamin Grosof. DARPA Agent Markup Language (DAML) Tools for Supporting Intelligent Annotation, Sharing and Retrieval. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada462785.

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Lorincz, Andras. Models of Distribution Computation: Behavior Characterization of Intelligent Problem Solving for an Agent Hierarchy in a Competitive (Web) Environment. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada637295.

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