Relevant bibliographies by topics / Intelligent Agents

Contents

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

Academic literature on the topic 'Intelligent Agents'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Intelligent Agents"

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

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Intelligent Agents"

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Suliman, Hussam. "Artificial intelligence for cognitive agents and intelligent environments." Thesis, University of Wolverhampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440235.

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The objective ofthis research is to create intelligent and cognitive agents that resemble animal or human characters and provide an architecture in which to do so. This requires computational models and the formulation of ontology for agents and their environments that is well structured for software and efficient computer simulation. This research concludes by presenting GOY A - an integrated cognitive agent architecture for the specification of cognitive agents simulated in virtual environments. Cognitive features modelled for GOYA include sensory and spatial perception, attention, and human memory are among the most important. The cognitive systems modelled aim to be practical, plausible, and contribute to produce believable and intelligent agents in virtual environments such as computer games.
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IMPERIAL, JULIANA CARPES. "TRUST IN INTELLIGENT AGENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=11487@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Confiança é um aspecto fundamental em sistemas distribuídos abertos de larga-escala. Ela está no núcleo de todas as interações entre as entidades que precisam operar em ambientes com muita incerteza e que se modificam constantemente. Dada essa complexidade, esses componentes, e o sistema resultante, são cada vez mais contextualizados, desenhados e construídos usando técnicas baseadas em agentes. Portanto, confiança é fundamental em um sistema multi-agentes (MAS) aberto. Logo, este trabalho investiga como se ter um modelo de confiança explicitamente em um agente inteligente, que possui crenças (Beliefs), desejos (Desires) e intenções (Intentions), chamado de agente BDI. Ou seja, o agente passa a ter um quarto componente chamado confiança (Trust). Dessa forma, é necessário uma lógica para englobar o conceito de confiança em um MAS BDI aberto. Isso é feito usando uma lógica multi-modal indexada, onde os mundos possíveis que modelam um sistema multi-agentes representam quais agentes estão presentes em um dado instante de tempo. E, para cada uma três componentes originais de um agente BDI, há também uma representação de mundos possíveis, pois as mesmas são tratadas como modalidades. Já a confiança é modelada como sendo um predicado, e não uma modalidade.
Trust is a fundamental concern in large-escale open distributed sytems. It lies at the core of all interactios between the entities that have to operate in such uncertain and constantly changing environmonts. Given the complexity of the interactions, these components, and the ensuing system, are increasingly being conceptualised, desined, and built using agent-based techiques. Therefore, the presence of trust is imperative in a multi-agent system (MAS). Consequently, this work studies how to have a explicit trust model in intelligent agent, which has beliefs, desires and intentions (BDI agent). Thas is, the agent now has a fourth component called Trust. This way, a logic to include the concept of trust in an open BDI MAS is interesting, so that the different aspects of a trust model can be expressed formally and accuratelly. This is achieved by using an indexed multi-modal logic, where the possible worlds which model a multi-agent system represent which agents are in the system in a given moment. Moreover, for each one of the three original components of a BDI agent, where the components represent beliefs, desires and intentions, there is a representation of possible worlds, because these are treated as modalities. However, trust is modelled as predicate, not as a modality.
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Chen, Hsinchun, Andrea L. Houston, Jerome Yen, and Jay F. Nunamaker. "Toward Intelligent Meeting Agents." IEEE, 1996. http://hdl.handle.net/10150/106163.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
An experiment with an AI-based software agent shows that it can help users organize and consolidate ideas from electronic brainstorming. The agent recalled concepts as effectively as experienced human meeting facilitators and in a fifth of the time.
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Løland, Karl Syvert. "Intelligent agents in computer games." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9759.

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In this project we examine whether or not a intelligent agent can learn how to play a computer game using the same inputs and outputs as a human. An agent architecture is chosen, implemented, and tested on a standard first person shooter game to see if it can learn how to play that game and find a goal in that game. We conclude the report by discussing potential improvements to the current implementation.

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Abouzakhar, Nasser Salem. "Intelligent agents-based networks security." Thesis, University of Sheffield, 2005. http://etheses.whiterose.ac.uk/3575/.

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The growing dependence of modem society on telecommunication and information networks has become inevitable. The increase in the number of networks interconnected over the Internet has led to an increase in security threats. The existing mobile and fixed network systems and telecommunication protocols are not appropriately designed to deal with current developed distributed attacks. I started my research work by exploring the deployment of intelligent Agents that could detect network anomalies and issue automated response actions. An Intelligent Agent (IA) [Knapik et at, 1998] is an entity that carries out some set of operations on behalf of a user or other software with some degree of independence or autonomy. The investigation of the Agents paradigm led to a deep understanding of the underlying problem; therefore, machine learning has turned my attention to Bayesian learning and Fuzzy logic approaches. A modelled network intrusion detector has been proposed. This model sets Agents with learning capabilities for detecting current as well as similar future distributed network attacks. In order to detect those anomalies as early as possible, the Bayesian network approach has been proposed. This approach is considered to be a promising method in determining suspicious network anomaly events that consequently relates them to subsequent dependent illegitimate activities. This research suggests innovative ways to develop Intelligent Agents that incorporate Bayesian learning to address network security risks associated with the current Networks Intrusion Detection Systems (NIDSs) designs and implementations. Because NIDSs have traditionally focused on detecting attacks, and while detection serves a vital purpose, it does not provide the ultimate solution. As aresult, an effective response mechanism to those detected attacks is required to minimise their effect and hence enhance NIDSs capabilities. Therefore, other Agents with Fuzzy intelligence capabilities have been proposed to initiate successful automated response actions. Fuzzy Agents have been proposed to handle this task with the ability to respond quickly and dynamically control the availability of allocated network resources. The evaluation methodology used to assess the performance of the developed models has been concentrated on detecting as well as predicting unauthorised activities in networks. By means of evaluation and validation, as well as empirical evidence, we are able to determine the effectiveness of the developed models and assumptions. The performance of developed detection model algorithms for unsupervised learning tasks has been evaluated using well known standard methods such as Confusion matrix. The achieved results indicate that the developed model led to a substantial reduction of the false alarms, with significant increase in the detection rates. This research work is operating within the context of two domains the first drawn from the network security community and the other from the machine learning community. It investigates the deployment of both Bayesian Learning as a probabilistic approach and Fuzzy Intelligence as a possibilistic approach to networks security. This is to detect as well as predict future evolving network anomalies, and to effectively respond to those developed attacks and minimise their effects. Consequently, it may provide innovative solutions that can be implemented in a cost-effective manner.
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Chan, Tung 1972. "Artificial markets and intelligent agents." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8924.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 173-178).
In many studies of market microstructure, theoretical analysis quickly becomes in tractable for all but the simplest stylized models. This thesis considers two alternative approaches, namely, the use of experiments with human subjects and simulations with intelligent agents, to address some of the limitations of theoretical modeling. The thesis aims to study the design, development and characterization of artificial markets as well as the behaviors and strategies of intelligent trading and market making agents. Simulations and experiments are conducted to study information aggregation and dissemination in a market. A number of features of the market dynamics are examined: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, and the learning dynamics of traders who possess diverse information or preferences.
(cont.) By constructing simple intelligent agents, not only am I able to replicate several findings of human-based experiments, but I also find intriguing differences between agent-based and human based experiments. The importance of liquidity in securities markets motivates considerable inter ests in studying the behaviors of market-makers. A rule-based market-maker, built in with multiple objectives, including maintaining a fair and orderly market, maximizing profit and minimizing inventory risk, is constructed and tested on historical transaction data. Following the same design, an adaptive market-maker is modeled in the framework of reinforcement learning. The agent is shown to be able to adapt its strategies to different noisy market environments.
by Tung Chan.
Ph.D.
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Mavity, Nick Jeremy. "Intelligent interface agents for biometric applications." Thesis, University of Kent, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429665.

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McInerney, James. "Intelligent agents for mobile location services." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/365495/.

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Understanding human mobility patterns is a significant research endeavour that has recently received considerable attention. Developing the science to describe and predict how people move from one place to another during their daily lives promises to address a wide range of societal challenges: from predicting the spread of infectious diseases, improving urban planning, to devising effective emergency response strategies. Individuals are also set to benefit from this area of research, as mobile devices will be able to analyse their mobility pattern and offer context-aware assistance and information. For example, a service could warn about travel disruptions before the user is likely to encounter them, or provide recommendations and mobile vouchers for local services that promise to be of high value to the user, based on their predicted future plans. More ambitiously, control systems for home heating and electric vehicle charging could be enhanced with knowledge of when the user will be home. In this thesis, we focus on such anticipatory computing. Some aspects of the vision of context-awareness have been pursued for many years, resulting in mature research in the area of ubiquitous systems. However, the combination of surprisingly rapid adoption of advanced mobile devices by consumers and the broad acceptance of location-based apps has surfaced not only new opportunities, but also a number of pressing challenges. In more detail, these challenges are the (i) prediction of future mobility, (ii) inference of features of human location behaviour, and (iii) use of prediction and inference to make decisions about timely information or control actions. Our research brings together, for the first time, the entire workflow that a mobile location service needs to follow, in order to achieve an understanding of mobile user needs and to act on such understanding effectively. This framing of the problem highlights the shortcomings of existing approaches which we seek to address. In the current literature, prediction is only considered for established users, which implicitly assumes that new users will continue to use an initially inaccurate prediction system long enough for it to improve and increase in accuracy over time. Additionally, inference of user behaviour is mostly concerned with interruptibility, which does not take into account the constructive role of intelligent location services that goes beyond simply avoiding interrupting the user at inopportune times (e.g., in a meeting, or while driving). Finally, no principled decision framework for intelligent location services has been provided that takes into account the results of prediction and inference. To address these shortcomings, we make three main contributions to the state of the art. Firstly, we provide a novel Bayesian model that relates the location behaviour of new and established users, allowing the reuse of structure learnt from rich mobility data. This model shows a factor of 2.4 improvement over the state-of-the-art baseline in heldout data likelihood in experiments using the Nokia Lausanne dataset. Secondly, we give new tools for the analysis and prediction of routine in mobility, which is a latent feature of human behaviour, that informs the service about the user’s availability to follow up on any information provided. And thirdly, we provide a fully worked example of an intelligent mobile location service (a crowdsourced package delivery service) that performs decision-making using predictive densities of current and future user mobility. Simulations using real mobility data from the Orange Ivory Coast dataset indicate a 81.3% improvement in service efficiency when compared with the next best (non-anticipatory) approach.
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Chen, Kristin M., and Hsinchun Chen. "Intelligent Software Agents for Electronic Commerce." Springer, 2000. http://hdl.handle.net/10150/106448.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Electronic commerce (EC) and software agents are two of the hottest fields of research in information science. As the Internet is rapidly becomes a popular marketplace for consumers and sellers of goods and services, combining these two research areas offers lucrative opportunities both for businesses wishing to conduct transactions over the World Wide Web (WWW) and for developers of tools to facilitate this trend. The focus in this chapter will be on software agents specifically designed for electronic commerce activities. We will briefly describe the history of agent research in general, defining characteristics of agents, and will touch on the different types of agents. Following this introduction we will describe the learning and action mechanisms that make it possible for agents to perform tasks. Finally, we will describe the issues associated with the deployment of electronic commerce agents (ECAs).
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Peña, de Carrillo Clara Inés. "Intelligent agents to improve adaptivity in a web-based learning environment." Doctoral thesis, Universitat de Girona, 2004. http://hdl.handle.net/10803/7725.

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En esta tesis se propone el uso de agentes inteligentes en entornos de aprendizaje en línea con el fin de mejorar la asistencia y motivación del estudiante a través de contenidos personalizados que tienen en cuenta el estilo de aprendizaje del estudiante y su nivel de conocimiento. Los agentes propuestos se desempeñan como asistentes personales que ayudan al estudiante a llevar a cabo las actividades de aprendizaje midiendo su progreso y motivación.
El entorno de agentes se construye a través de una arquitectura multiagente llamada MASPLANG diseñada para dar soporte adaptativo (presentación y navegación adaptativa) a un sistema hipermedia educativo desarrollado en la Universitat de Girona para impartir educación virtual a través del web.
Un aspecto importante de esta propuesta es la habilidad de construir un modelo de estudiante híbrido que comienza con un modelo estereotípico del estudiante basado en estilos de aprendizaje y se modifica gradualmente a medida que el estudiante interactúa con el sistema (gustos subjetivos).
Dentro del contexto de esta tesis, el aprendizaje se define como el proceso interno que, bajo factores de cambio resulta en la adquisición de la representación interna de un conocimiento o de una actitud. Este proceso interno no se puede medir directamente sino a través de demostraciones observables externas que constituyen el comportamiento relacionado con el objeto de conocimiento. Finalmente, este cambio es el resultado de la experiencia o entrenamiento y tiene una durabilidad que depende de factores como la motivación y el compromiso.
El MASPLANG está compuesto por dos niveles de agentes: los intermediarios llamados IA (agentes de información) que están en el nivel inferior y los de Interfaz llamados PDA (agentes asistentes) que están en el nivel superior. Los agentes asistentes atienden a los estudiantes cuando trabajan con el material didáctico de un curso o una lección de aprendizaje. Esta asistencia consiste en la recolección y análisis de las acciones de los estudiantes para ofrecer contenidos personalizados y en la motivación del estudiante durante el aprendizaje mediante el ofrecimiento de contenidos de retroalimentación, ejercicios adaptados al nivel de conocimiento y mensajes, a través de interfaces de usuario animadas y atractivas. Los agentes de información se encargan del mantenimiento de los modelos pedagógico y del dominio y son los que están en completa interacción con las bases de datos del sistema (compendio de actividades del estudiante y modelo del dominio).
El escenario de funcionamiento del MASPLANG está definido por el tipo de usuarios y el tipo de contenidos que ofrece. Como su entorno es un sistema hipermedia educativo, los usuarios se clasifican en profesores quienes definen y preparan los contenidos para el aprendizaje adaptativo, y los estudiantes quienes llevan a cabo las actividades de aprendizaje de forma personalizada. El perfil de aprendizaje inicial del estudiante se captura a través de la evaluación del cuestionario ILS (herramienta de diagnóstico del modelo FSLSM de estilos de aprendizaje adoptado para este estudio) que se asigna al estudiante en su primera interacción con el sistema. Este cuestionario consiste en un conjunto de preguntas de naturaleza sicológica cuyo objetivo es determinar los deseos, hábitos y reacciones del estudiante que orientarán la personalización de los contenidos y del entorno de aprendizaje. El modelo del estudiante se construye entonces teniendo en cuenta este perfil de aprendizaje y el nivel de conocimiento obtenido mediante el análisis de las acciones del estudiante en el entorno.
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Books on the topic "Intelligent Agents"

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Resconi, Germano, and Lakhmi C. Jain. Intelligent Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44401-5.

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Wooldridge, Michael J., and Nicholas R. Jennings, eds. Intelligent Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-58855-8.

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Pelachaud, Catherine, Jean-Claude Martin, Elisabeth André, Gérard Chollet, Kostas Karpouzis, and Danielle Pelé, eds. Intelligent Virtual Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74997-4.

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Bickmore, Timothy, Stacy Marsella, and Candace Sidner, eds. Intelligent Virtual Agents. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09767-1.

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Vilhjálmsson, Hannes Högni, Stefan Kopp, Stacy Marsella, and Kristinn R. Thórisson, eds. Intelligent Virtual Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23974-8.

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de Antonio, Angélica, Ruth Aylett, and Daniel Ballin, eds. Intelligent Virtual Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44812-8.

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Aylett, Ruth, Brigitte Krenn, Catherine Pelachaud, and Hiroshi Shimodaira, eds. Intelligent Virtual Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40415-3.

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Brenner, Walter, Rüdiger Zarnekow, and Hartmut Wittig. Intelligent Software Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80484-7.

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Traum, David, William Swartout, Peter Khooshabeh, Stefan Kopp, Stefan Scherer, and Anton Leuski, eds. Intelligent Virtual Agents. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47665-0.

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Panayiotopoulos, Themis, Jonathan Gratch, Ruth Aylett, Daniel Ballin, Patrick Olivier, and Thomas Rist, eds. Intelligent Virtual Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550617.

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Book chapters on the topic "Intelligent Agents"

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Tolle, Kristin M. "Intelligent Agents." In Systems Development Methods for the Next Century, 275–90. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5915-3_23.

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Skilton, Mark, and Felix Hovsepian. "Intelligent Agents." In The 4th Industrial Revolution, 99–119. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62479-2_4.

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Chowdhary, K. R. "Intelligent Agents." In Fundamentals of Artificial Intelligence, 471–505. New Delhi: Springer India, 2020. http://dx.doi.org/10.1007/978-81-322-3972-7_16.

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Agarwal, Rashi, Supriya Khaitan, and Shashank Sahu. "Intelligent Agents." In Distributed Artificial Intelligence, 19–46. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-2.

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Kore, Akshay. "Intelligent Agents." In Designing Human-Centric AI Experiences, 27–44. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8088-1_2.

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O'Callaghan, Miriam. "Intelligent Agents." In Decision Intelligence, 89–126. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23322-4.

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Kordon, Arthur K. "Intelligent Agents: The Computer Intelligence Agency (CIA)." In Applying Computational Intelligence, 175–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69913-2_7.

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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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Resconi, Germano, and Lakhmi C. Jain. "Evolutionary Adaptive Agents." In Intelligent Agents, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44401-5_1.

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Resconi, Germano, and Lakhmi C. Jain. "Practical Applications of Agents in Robots and Evolution Population." In Intelligent Agents, 377–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44401-5_10.

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Conference papers on the topic "Intelligent Agents"

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Skolicki, Zbigniew, and Tomasz Arciszewski. "Intelligent Agents in Design." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dtm-48671.

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In this paper we argue that learning or adaptation ability should be included in the basic set of features characterizing an intelligent agent in design. We propose a collection of attributes describing agents, which are grouped into several categories. Next, we present the results of a detailed study of all agents for design, which were discussed during the First International Workshop on Agents in Design at MIT in August of 2002. A statistical analysis of their attributes has been conducted and its results are reported to suggest future evolution of agents in design. Finally, we briefly overview the topic of Directed Evolution and use its paradigms to predict further development of agents in design. The paper also provides our initial conclusions and suggests further research.
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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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Patrut, Bogdan. "E-TEACHING AND E-ASSESSMENT IN ACCOUNTING USING INTELLIGENT PEDAGOGICAL AGENTS." In eLSE 2013. Carol I National Defence University Publishing House, 2013. http://dx.doi.org/10.12753/2066-026x-13-081.

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Internationally, there are several preoccupations concerning accounting training by the use of ICTs, no matter if they are performed in an institutionalized environment or not. The creation of an intelligent training system requires the incorporation of certain software elements with initiative, a certain humanized intelligent agent. These elements are very important in humanizing the artificial process, leading thus to the idea of using intelligent pedagogical agents in educational software. Intelligent pedagogical agents can express human-like feelings; they can learn the user?s interests, habits and preferences, and display human and social behavior, or even they can substitute some of the teacher?s features. ContTest is a multi-agent system for teaching and learning accounting. It uses a group of intelligent agents for initial and constant assessment, for teaching and illustrating, for generating tests, for checking answers, and for the final assessment and grading. Some of the agents used in ContTest are pedagogical agents. The pedagogical agents use both animated artificial characters and video-sequences with real teachers. In this paper, we will proof that the combined use of these technologies is very useful. The animated artificial character, although less human in aspect, can be more flexibly designed and can have a bidirectional message exchange with the learner. On the other hand, pre-made video sequences, although more rigid, will give the impression of a person?s real presence that instructs the student in an interactive manner. Our paper will present the architecture of the general system, with focuses on the combination of the both type of pedagogical agents. Also, we will present the results of using the pedagogical agents of the ContTest system in a class of students at ?Accounting and Business Information Systems?.
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Kalyanakrishnan, Shivaram. "Intelligent and Learning Agents: Four Investigations." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/700.

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My research is driven by my curiosity about the nature of intelligence. Of the several aspects that characterise the behaviour of intelligent agents, I primarily study sequential decision making, learning, and exploration. My interests also extend to broader questions on the effects of AI on life and society. In this paper, I present four distinct investigations drawn from my recent work, which range from theoretical to applied, and which involve both analysis and design. I also share my outlook as an early-career researcher.
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Cantrell, Bradley, and Zihao Zhang. "Choreographing Intelligent Agents." In 107th ACSA Annual Meeting. ACSA Press, 2019. http://dx.doi.org/10.35483/acsa.am.107.1.

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Radecký, Michal, and Petr Gajdoš. "Reconfigurable Intelligent Agents." In Tenth International Conference on Computer Modeling and Simulation (uksim 2008). IEEE, 2008. http://dx.doi.org/10.1109/uksim.2008.138.

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Lieberman, Henry. "Intelligent interface agents." In the 4th international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/291080.291083.

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"Session: Intelligent Agents." In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2019. http://dx.doi.org/10.1109/iccp48234.2019.8959740.

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Ballestas, Caseysimone, Euiyoung Kim, Jesuël Lanoy, and Jules Janssens. "Design-Engineers’ Selection of Agency: Harm Mitigation in Ambient Intelligent Environments." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91063.

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Abstract The computing paradigm where sensor and actuator technology work in tandem to track and act on events in real Euclidean space, known as ambient intelligence (AmI), is likely to become increasingly common due to the rapid maturation of computing technology. Installing AmI in the built environment creates ambient intelligent environments (AmIE), which strive to make the places we inhabit (invisibly) sensitive and responsive to our presence, needs, wants, and preferences. Given that built environments and the goings-on therein are complicated in an of them selves, implementing AmI for (increasingly) complicated tasks in (increasingly) complicated scenarios, increases the difficulty of managing the outcomes in AmIEs. Our previous research indicates that industry practitioners attribute the agency of AmI artifacts as responsible for these outcomes; especially when harm perpetuation is (one of) the outcome(s), which we codified as the Agency/Intelligence Axis [1]. Due to the nascence of AmI, research on best practices for the design-engineering of AmI is still emerging. This research seeks to add to this literature by evaluating our formerly identified Agency/Intelligence Axis in the context of AmIE through a case study of VyZee, a retail company working on transitioning their retail stores to “smart” stores. Our findings highlight that while VyZee seems largely unaware of any relationship between agency and perpetuating un-anticipated/-desired outcomes, they do implement an array of levels of AmI agency in their retail stores, and their justifications for their choices are presented in the discussion. Finally, coding the data revealed more nuance then previously documented in the Agency/Intelligence Axis, and a new Ambient Intelligent Agent Model, which suggests that AmI agents have six properties, is proposed.
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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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Reports on the topic "Intelligent Agents"

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Wiederhold, Gio, Rudi Studer, Mark Musen, Stefan Decker, and Steffen Staab. Onto-Agents-Enabling Intelligent Agents on the Web. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435112.

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Burmester, Mike, and Alex Yasinsac. Secure Tactical Mobile Intelligent Agents. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada427578.

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Pertzborn, Amanda J. Intelligent Building Agents Laboratory: Hydronic System Design. National Institute of Standards and Technology, September 2016. http://dx.doi.org/10.6028/nist.tn.1933.

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Pertzborn, Amanda J., and Daniel A. Veronica. Intelligent building agents laboratory: air system design. Gaithersburg, MD: National Institute of Standards and Technology, September 2018. http://dx.doi.org/10.6028/nist.tn.2025.

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Hayes-Roth, Barbara, Lee Brownston, and Anne Collinot. Strategic Control of Reactive Behavior in Intelligent Agents. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada264376.

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Knoblock, Craig A. Intelligent Agents for Retrieving, Filtering, and Managing Information. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada387501.

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Bowman, Michael. Center of Gravity Analysis: Preparing for Intelligent Agents. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada393165.

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Scheiber, Lane B., and Ronald A. Enlow. Warfighter's Edge: Using Intelligent Agents To Solve Warfighter Problems. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada390191.

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Pertzborn, Amanda J., and Daniel A. Veronica. Baseline Control Systems in the Intelligent Building Agents Laboratory. National Institute of Standards and Technology, September 2022. http://dx.doi.org/10.6028/nist.tn.2178.

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Chen, Harry, Filip Perich, Dipanjan Chakraborty, Tim Finin, and Anupam Joshi. Intelligent Agents Meet Semantic Web in a Smart Meeting Room. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439732.

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