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Journal articles on the topic 'Intelligent Agents'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Singh, Sushil. "Universal Artificial Intelligence for Intelligent Agents: An Approach to Super Intelligent Agents." IOSR Journal of Computer Engineering 12, no. 6 (2013): 43–48. http://dx.doi.org/10.9790/0661-1264348.

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2

Kozma, J. "Intelligent agents." IEEE Potentials 17, no. 2 (1998): 16–19. http://dx.doi.org/10.1109/45.666640.

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3

Sarma, V. V. S. "Intelligent Agents." IETE Journal of Research 42, no. 3 (May 1996): 105–9. http://dx.doi.org/10.1080/03772063.1996.11415911.

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4

Riecken, Doug. "Intelligent agents." Communications of the ACM 37, no. 7 (July 1994): 18–21. http://dx.doi.org/10.1145/176789.176790.

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5

Rodriguez, Walter, Janusz Zalewski, and Elias Kirche. "Beyond Intelligent Agents." International Journal of e-Collaboration 3, no. 2 (April 2007): 1–15. http://dx.doi.org/10.4018/jec.2007040101.

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6

Nadrljanski, Mila, and V. Batinica. "Intelligent media agents." International Journal of Intelligent Defence Support Systems 3, no. 1/2 (2010): 128. http://dx.doi.org/10.1504/ijidss.2010.033682.

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7

Jeapes, Ben. "NEURAL INTELLIGENT AGENTS." Online and CD-Rom Review 20, no. 5 (May 1996): 260–62. http://dx.doi.org/10.1108/eb024592.

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8

Sycara, K., A. Pannu, M. Willamson, Dajun Zeng, and K. Decker. "Distributed intelligent agents." IEEE Expert 11, no. 6 (December 1996): 36–46. http://dx.doi.org/10.1109/64.546581.

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9

Vasilakos, A. V. "Intelligent Information Agents." Computer Communications 23, no. 18 (December 2000): 1790. http://dx.doi.org/10.1016/s0140-3664(00)00211-5.

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10

Wong, A. "Intelligent Software Agents." Computer Communications 23, no. 7 (March 2000): 695–96. http://dx.doi.org/10.1016/s0140-3664(99)00186-3.

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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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12

Valckenaers, Paul, Hadeli, Bart Saint Germain, Paul Verstraete, Jan van Belle, and Hendrik van Brussel. "FROM INTELLIGENT AGENTS TO INTELLIGENT BEINGS." IFAC Proceedings Volumes 40, no. 18 (September 2007): 31–36. http://dx.doi.org/10.3182/20070927-4-ro-3905.00007.

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13

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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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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15

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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16

Desouza, Kevin C. "Intelligent agents for competitive intelligence: Survey of applications." Competitive Intelligence Review 12, no. 4 (2001): 57–63. http://dx.doi.org/10.1002/cir.1032.

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17

Lizán, Francisco J. Mora. "Intelligent Buildings: Foundation for Intelligent Physical Agents." International Journal of Engineering Research and Applications 07, no. 05 (May 2017): 21–25. http://dx.doi.org/10.9790/9622-0705022125.

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18

Bryndin, Evgeny. "About Formation of International Ethical Digital Environment with Smart Artificial Intelligence." International Journal of Information Technology, Control and Automation 11, no. 1 (January 31, 2021): 1–9. http://dx.doi.org/10.5121/ijitca.2021.11101.

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Intellectual agent ensembles allow you to create digital environment by professional images with language, behavioral and active communications, when images and communications are implemented by agents with smart artificial intelligence. Through language, behavioral and active communications, intellectual agents implement collective activities. The ethical standard through intelligent agents allows you to regulate the safe use of ensembles made of robots and digital doubles with creative communication artificial intelligence in the social sphere, industry and other professional fields. The use of intelligent agents with smart artificial intelligence requires responsibility from the developer and owner for harming others. If harm to others occurred due to the mistakes of the developer, then he bears responsibility and costs. If the damage to others occurred due to the fault of the owner due to non-compliance with the terms of use, then he bears responsibility and costs. Ethical standard and legal regulation help intellectual agents with intelligent artificial intelligence become professional members of society. Ensembles of intelligent agents ith smart artificial intelligence will be able to safely work with society as professional images with skills, knowledge and competencies, implemented in the form of retrained digital twins and cognitive robots that interact through language, behavioral and active ethical communications. Cognitive robots and digital doubles through self-developing ensembles of intelligent agents with synergistic interaction and intelligent artificial intelligence can master various high-tech professions and competencies. Their use in the industry increases labor productivity and economic efficiency of production. Their application in the social sphere improves the quality of life of a person and society. Their widespread application requires compliance with an ethical standard so that their use does not cause harm. The introduction and use of an ethical standard for the use of cognitive robots and digital doubles with smart artificial intelligence increases the safety of their use. Ethical relationships between individuals and intellectual agents will also be governed by an ethical standard.

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19

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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20

Edmonds, Bruce. "MODELING SOCIALLY INTELLIGENT AGENTS." Applied Artificial Intelligence 12, no. 7-8 (October 1998): 677–99. http://dx.doi.org/10.1080/088395198117587.

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21

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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22

Harris, L. E. "Intelligent agents peer skyward." Computing in Science & Engineering 6, no. 5 (September 2004): 4–7. http://dx.doi.org/10.1109/mcse.2004.40.

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23

Nardi, Bonnie A., James R. Miller, and David J. Wright. "Collaborative, programmable intelligent agents." Communications of the ACM 41, no. 3 (March 1998): 96–104. http://dx.doi.org/10.1145/272287.272331.

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24

Sukhatme, Gaurav S. "Intelligent embodied autonomous agents." Robotics and Autonomous Systems 29, no. 2-3 (November 1999): 109–10. http://dx.doi.org/10.1016/s0921-8890(99)00045-7.

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25

Jamal, Karim, Michael Maier, and Shyam Sunder. "Simple Agents, Intelligent Markets." Computational Economics 49, no. 4 (May 11, 2016): 653–75. http://dx.doi.org/10.1007/s10614-016-9582-3.

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26

Hsinchun Chen, A. Houston, J. Nunamaker, and J. Yen. "Toward intelligent meeting agents." Computer 29, no. 8 (1996): 62–70. http://dx.doi.org/10.1109/2.532047.

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27

Serenko, Alexander, and Brian Detlor. "Intelligent agents as innovations." AI & SOCIETY 18, no. 4 (September 24, 2004): 364–81. http://dx.doi.org/10.1007/s00146-004-0310-5.

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28

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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29

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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30

Brinkschulte, Luisa, Stephan Schlögl, Alexander Monz, Pascal Schöttle, and Matthias Janetschek. "Perspectives on Socially Intelligent Conversational Agents." Multimodal Technologies and Interaction 6, no. 8 (July 25, 2022): 62. http://dx.doi.org/10.3390/mti6080062.

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The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to the exploration of relevant traits and their potential embedding in future agent technology. Aiming to lay a foundation for further developments, we report on the results of a Delphi study highlighting the respective opinions of 21 multi-disciplinary domain experts. Results exhibit 14 distinctive characteristics of social intelligence, grouped into different levels of consensus, maturity, and abstraction, which may be considered a relevant basis, assisting the definition and consequent development of socially intelligent conversational agents.

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31

Mackworth, Alan K. "Agents, Bodies, Constraints, Dynamics, and Evolution." AI Magazine 30, no. 1 (January 18, 2009): 7. http://dx.doi.org/10.1609/aimag.v30i1.2174.

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The theme of this article is the dynamics of evolution of agents. That theme is applied to the evolution of constraint satisfaction, of agents themselves, of our models of agents, of artificial intelligence and, finally, of the Association for the Advancement of Artificial Intelligence (AAAI). The overall thesis is that constraint satisfaction is central to proactive and responsive intelligent behavior.

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32

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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33

Dignum, F., J. Westra, W. A. van Doesburg, and M. Harbers. "Games and Agents: Designing Intelligent Gameplay." International Journal of Computer Games Technology 2009 (2009): 1–18. http://dx.doi.org/10.1155/2009/837095.

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There is an attention shift within the gaming industry toward more natural (long-term) behavior of nonplaying characters (NPCs). Multiagent system research offers a promising technology to implement cognitive intelligent NPCs. However, the technologies used in game engines and multiagent platforms are not readily compatible due to some inherent differences of concerns. Where game engines focus on real-time aspects and thus propagate efficiency and central control, multiagent platforms assume autonomy of the agents. Increased autonomy and intelligence may offer benefits for a more compelling gameplay and may even be necessary for serious games. However, it raises problems when current game design techniques are used to incorporate state-of-the-art multiagent system technology. In this paper, we will focus on three specific problem areas that arise from this difference of view: synchronization, information representation, and communication. We argue that the current attempts for integration still fall short on some of these aspects. We show that to fully integrate intelligent agents in games, one should not only use a technical solution, but also a design methodology that is amenable to agents. The game design should be adjusted to incorporate the possibilities of agents early on in the process.

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34

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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35

BAILIN, SIDNEY C., and WALT TRUSZKOWSKI. "Ontology negotiation between intelligent information agents." Knowledge Engineering Review 17, no. 1 (March 2002): 7–19. http://dx.doi.org/10.1017/s0269888902000292.

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This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assumptions – to communicate in solving tasks when they encounter each other on the web. In particular, we have focused on information search tasks.We have developed a protocol that allows agents to discover ontology conflicts and then, through incremental interpretation, clarification and explanation, establish a common basis for communicating with each other. We have implemented this protocol in a set of Java classes that can be added to a variety of agents, irrespective of their underlying ontological assumptions. We have demonstrated the use of the protocol, through this implementation, in a test-bed that includes two large scientific archives: NASA's Global Change Master Directory and NOAA's Wind and Sea Index.This paper presents an overview of different methods for resolving ontology mismatches and motivates the Ontology Negotiation Protocol (ONP) as a method that addresses some problems with other approaches. Much remains to be done. The protocol must be tested in larger and less familiar contexts (for example, numerous archives that have not been preselected) and it must be extended to accommodate additional forms of clarification and ontology evolution.

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36

Uriarte, Alberto, and Santiago Ontañón. "A Benchmark for StarCraft Intelligent Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 2 (June 24, 2021): 22–28. http://dx.doi.org/10.1609/aiide.v11i2.12810.

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The problem of comparing the performance of different Real-Time Strategy (RTS) Intelligent Agents (IA) is non-trivial. And often different research groups employ different testing methodologies designed to test specific aspects of the agents. However, the lack of a standard process to evaluate and compare different methods in the same context makes progress assessment difficult. In order to address this problem, this paper presents a set of benchmark scenarios and metrics aimed at evaluating the performance of different techniques or agents for the RTS game StarCraft. We used these scenarios to compare the performance of a collection of bots participating in recent StarCraft AI (Artificial Intelligence) competitions to illustrate the usefulness of our proposed benchmarks.

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37

Binkley, Timothy. "Autonomous Creations: Birthing Intelligent Agents." Leonardo 31, no. 5 (1998): 333. http://dx.doi.org/10.2307/1576591.

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38

Walden, Pirkko, Christer Carlsson, and Shuhua Liu. "Industry foresight with intelligent agents." Human Systems Management 19, no. 3 (July 8, 2000): 169–80. http://dx.doi.org/10.3233/hsm-2000-19304.

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Modern time managers have access to many more data sources than managers of earlier times, and better instruments and resources to deal with large amounts of data. In principle, this means that they have a better command of facts and should be able to work out better assessments of their business environment. In reality, however, information overflow and problems with the quality and reliability of information complicate the picture. We have a support system with intelligent agents to help managers conduct constantly active scanning and interpretation activities with hundreds of data sources. The system was built on a Java platform and has been enhanced and developed in several versions. The first implementation was at the Alko Group (the producers of the Finlandia vodka). The system is expected to provide mangers with a broad and comprehensive first approximation of environmental trends and events as needed, and will help them extract useful information from large volumes of data. (Originally presented at the ISDSS’99 Conference, Melbourne, July 19–22, 1999.)

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AMANDI, Analía, Marcelo CAMPO, Marcelo ARMENTANO, and Luis BERDUN. "Intelligent Agents for Distance Learning." Informatics in Education 2, no. 2 (October 15, 2003): 161–80. http://dx.doi.org/10.15388/infedu.2003.12.

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40

Agarwal, Rakesh, Amrita Deo, and Swati Das. "Intelligent agents in E-learning." ACM SIGSOFT Software Engineering Notes 29, no. 2 (March 2004): 1. http://dx.doi.org/10.1145/979743.979755.

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Yilmaz, Levent, Tuncer Ören, and Nasser-Ghasem Aghaee. "Intelligent agents, simulation, and gaming." Simulation & Gaming 37, no. 3 (September 2006): 339–49. http://dx.doi.org/10.1177/1046878106289089.

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42

Locke, Chris. "INTELLIGENT AGENTS CREATE DUMB USERS (?)." Online and CD-Rom Review 21, no. 6 (June 1997): 369–72. http://dx.doi.org/10.1108/eb024644.

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Hughes, E., and M. Lewis. "Intelligent agents for radar systems." Electronics Systems and Software 3, no. 1 (February 1, 2005): 39–43. http://dx.doi.org/10.1049/ess:20050107.

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Duff, Simon, John Thangarajah, and James Harland. "MAINTENANCE GOALS IN INTELLIGENT AGENTS." Computational Intelligence 30, no. 1 (September 23, 2012): 71–114. http://dx.doi.org/10.1111/coin.12000.

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45

Braynov, Sviatoslav. "Coalitions of malicious intelligent agents." International Journal of Web Engineering and Technology 1, no. 4 (2004): 397. http://dx.doi.org/10.1504/ijwet.2004.006266.

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46

Feliu, S. "Intelligent Agents and Consumer Protection." International Journal of Law and Information Technology 9, no. 3 (September 1, 2001): 235–48. http://dx.doi.org/10.1093/ijlit/9.3.235.

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47

Dumitrache, I., Simona Iuliana Caramihai, Janetta Culiţă, C. Munteanu, and A. M. Stănescu. "Intelligent Autonomous Agents for Manufacturing." IFAC Proceedings Volumes 34, no. 8 (July 2001): 493–98. http://dx.doi.org/10.1016/s1474-6670(17)40864-0.

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48

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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49

Race, Patrick, Jason Tedor, and Kata L. Nance. "Intelligent Agents for Decision Support." IFAC Proceedings Volumes 31, no. 29 (October 1998): 94–97. http://dx.doi.org/10.1016/s1474-6670(17)38367-2.

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Race, Patrick, and Kara L. Nance. "Intelligent Agents for Decision Support." IFAC Proceedings Volumes 31, no. 29 (October 1998): 251–55. http://dx.doi.org/10.1016/s1474-6670(17)38953-x.

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