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Letteratura scientifica selezionata sul tema "Artificial adaptive agents"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 12 febbraio 2022

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Articoli di riviste sul tema "Artificial adaptive agents"

Andreoni, James, and John H. Miller. "Auctions with Artificial Adaptive Agents." Games and Economic Behavior 10, no. 1 (July 1995): 39–64. http://dx.doi.org/10.1006/game.1995.1024.

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Maes, Pattie. "Modeling Adaptive Autonomous Agents." Artificial Life 1, no. 1_2 (October 1993): 135–62. http://dx.doi.org/10.1162/artl.1993.1.1_2.135.

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Abstract (sommario):

One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of Artificial Intelligence (AI), which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought

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Wan, Hakman A., and Andrew Hunter. "On Artificial Adaptive Agents Models of Stock Markets." SIMULATION 68, no. 5 (May 1997): 279–89. http://dx.doi.org/10.1177/003754979706800503.

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Steels, Luc. "The Artificial Life Roots of Artificial Intelligence." Artificial Life 1, no. 1_2 (October 1993): 75–110. http://dx.doi.org/10.1162/artl.1993.1.1_2.75.

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Behavior-oriented Artificial Intelligence (AI) is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximizing their own self-preservation in interaction with a dynamically changing environment. The paper addresses this Artificial Life route toward AI and reviews some of the results obtained so far.

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MERTOGUNO, J. SUKARNO. "DISTRIBUTED KNOWLEDGE-BASE: ADAPTIVE MULTI-AGENTS APPROACH." International Journal on Artificial Intelligence Tools 07, no. 01 (March 1998): 59–70. http://dx.doi.org/10.1142/s0218213098000056.

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This paper describes an approach of using Multi-agents theory to construct an adaptive knowledge base system. To represent the knowledge, frame base (graph) knowledge representation has been chosen. The driving force of this study is the intention of having a distributed (possibly across the net) adaptive knowledge base. The challenge of developing an adaptive knowledge base lays on how to evolve the knowledge (adaptability) and how to control the evolution (maintain the quality of the knowledge). In this paper the manifestation of both of the issues above on our model will be addressed.

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

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects

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Karpov, Valery. "On moral aspects of adaptive behavior of artificial agents." Artificial societies 16, no. 2 (2021): 0. http://dx.doi.org/10.18254/s207751800014740-3.

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This article describes a model of a social agent, whose behavior can be stated in terms of basic moral mechanisms and norms. Morality is considered here as a flexible adaptive mechanism that allows agents to vary behavior depending on the environment conditions. The control system of the social agent is based on the &quot;emotion-requirement&quot; architecture. Together with the mechanisms of imitative behavior and the identification of other observable agents with the subjective &quot;Me&quot; concept, this architecture allows to interpret the agent&apos;s behavior in term

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Pereira, Ademir Rodrigues, and Liu Hsu. "Adaptive Formation Control using Artificial Potentials for Euler-Lagrange Agents." IFAC Proceedings Volumes 41, no. 2 (2008): 10788–93. http://dx.doi.org/10.3182/20080706-5-kr-1001.01829.

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Bullard, James, and John Duffy. "A model of learning and emulation with artificial adaptive agents." Journal of Economic Dynamics and Control 22, no. 2 (February 1998): 179–207. http://dx.doi.org/10.1016/s0165-1889(97)00072-9.

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Vrancx, Peter, Enda Howley, and Matt Knudson. "Preface to the special issue: adaptive learning agents." Knowledge Engineering Review 31, no. 1 (January 2016): 1–2. http://dx.doi.org/10.1017/s0269888915000144.

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Tesi sul tema "Artificial adaptive agents"

Neves, Pedro. "An Implementation Framework for Emotion Based Adaptive Agents." Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/14023.

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The work presented in this document is part of the project AutoFocus: Adaptive Self-Improving Multi-Agent Systems' that is being developed at the research unit LabMAg, which objective is the implementation of multi-agent systems based on autonomous entities capable of self-optimized and adaptive behaviors. The notion of autonomic computation, like other notions that also imply pro-active computation, is based on autonomous entities that actively work to achieve their objectives and have the ability to dynamically adjust to changes in their environment, constrained by time and resource limits.

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Rylatt, R. Mark. "Investigations into controllers for adaptive autonomous agents based on artificial neural networks." Thesis, De Montfort University, 2001. http://hdl.handle.net/2086/10734.

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This thesis reports the development and study of novel architectures for the simulation of adaptive behaviour based on artificial neural networks. There are two distinct levels of enquiry. At the primary level, the initial aim was to design and implement a unified architecture integrating sensorimotor learning and overall control. This was intended to overcome shortcomings of typical behaviour-based approaches in reactive control settings. It was achieved in two stages. Initially, feedforward neural networks were used at the sensorimotor level of a modular architecture and overall control was

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Bagnall, A. J. "Modelling the UK market in electricity generation with autonomous adaptive agents." Thesis, University of East Anglia, 2000. https://ueaeprints.uea.ac.uk/21583/.

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The modern trend in electricity industries around the world is towards privatisation. Increased competition, it is argued, will ultimately benefit the consumer. However, the particular nature of electricity generation and supply means strong regulation of a privatised market will always be necessary. In establishing a privatised industry, decisions need to be made about the mechanisms governing the requirements to meet demand, to maintain the viability of the network and to ensure generators are paid correctly for power generated. Unfortunately, it is unclear what processes to use to achieve t

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Fent, Thomas. "Adaptive agents in the House of Quality." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1556/1/document.pdf.

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Managing the information flow within a big organization is a challenging task. Moreover, in a distributed decision-making process conflicting objectives occur. In this paper, artificial adaptive agents are used to analyze this problem. The decision makers are implemented as Classifier Systems, and their learning process is simulated by Genetic Algorithms. To validate the outcomes we compared the results with the optimal solutions obtained by full enumeration. It turned out that the genetic algorithm indeed was able to generate useful rules that describe how the decision makers involved in new

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Fountoukidis, Dimitrios P. "Adaptive management of emerging battlefield network." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FFountoukidis.pdf.

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Thesis (M.S. in Information Technology Management and M.S. in Modeling Virtual Environment and Simulation)--Naval Postgraduate School, March 2004.<br>Thesis advisor(s): Alex Bordetsky, John Hiles. Includes bibliographical references. Also available online.

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Mazzapioda, Mariagiovanna. "On the evolutionary co-adaptation of morphology and distributed neural controllers in adaptive agents." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1011.

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The attempt to evolve complete embodied and situated artificial creatures in which both morphological and control characteristics are adapted during the evolutionary process has been and still represents a long term goal key for the artificial life and the evolutionary robotics community. Loosely inspired by ancient biological organisms which are not provided with a central nervous system and by simple organisms such as stick insects, this thesis proposes a new genotype encoding which allows development and evolution of mor- phology and neural controller in artificial agents provided with a di

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Kochenderfer, Mykel J. "Adaptive modelling and planning for learning intelligent behaviour." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/1408.

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An intelligent agent must be capable of using its past experience to develop an understanding of how its actions affect the world in which it is situated. Given some objective, the agent must be able to effectively use its understanding of the world to produce a plan that is robust to the uncertainty present in the world. This thesis presents a novel computational framework called the Adaptive Modelling and Planning System (AMPS) that aims to meet these requirements for intelligence. The challenge of the agent is to use its experience in the world to generate a model. In problems with large st

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Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.

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In this work, I view incremental experiential learning in intelligent software agents as progressive agent self-adaptation. When an agent produces an incorrect behavior, then it may reflect on, and thus diagnose and repair, the reasoning and knowledge that produced the incorrect behavior. In particular, I focus on the self-diagnosis and self-repair of an agent's domain knowledge. The implementation of systems with the capability to self-diagnose and self-repair involves building both reasoning processes capable of such learning and knowledge representations capable of supporting those reaso

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Schäfer, Boris. "Detached tool use in evolutionary robotics : Evolving tool use skills." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12.

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<p>This master thesis investigates the principal capability of artificial evolution to produce tool use behavior in adaptive agents, excluding the application of life-time learning or adaptation mechanisms. Tool use is one aspect of complex behavior that is expected from autonomous agents acting in real-world environments. In order to achieve tool use behavior an agent needs to identify environmental objects as potential tools before it can use the tools in a problem-solving task. Up to now research in robotics has focused on life-time learning mechanisms in order to achieve this. However, the

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Angus, Simon Douglas Economics Australian School of Business UNSW. "Economic networks: communication, cooperation & complexity." Awarded by:University of New South Wales. Economics, 2007. http://handle.unsw.edu.au/1959.4/27005.

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This thesis is concerned with the analysis of economic network formation. There are three novel sections to this thesis (Chapters 5, 6 and 8). In the first, the non-cooperative communication network formation model of Bala and Goyal (2000) (BG) is re-assessed under conditions of no inertia. It is found that the Strict Nash circle (or wheel) structure is still the equilibrium outcome for n = 3 under no inertia. However, a counter-example for n = 4 shows that with no inertia infinite cycles are possible, and hence the system does not converge. In fact, cycles are found to quickly dominate outcom

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Libri sul tema "Artificial adaptive agents"

Karl, Tuyls, ALAMAS 2006 (2006 : Brussels, Belgium), and ALAMAS 2007 (2007 : Maastricht, Netherlands), eds. Adaptive agents and multi-agent systems III: Adaptation and multi-agent learning ; 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems : revised selected papers. Berlin: Springer, 2008.

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Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. Leicester: De Montfort University, 2001.

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author, Shi Dianxi, ed. Ruan jian shi ying xing ji shu: Cong ge ti shi ying dao qun ti shi ying. Beijing: Ke xue chu ban she, 2013.

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Sugumaran, Vijayan. Intelligent, adaptive and reasoning technologies: New developments and applications. Hershey, PA: Information Science Reference, 2011.

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Matthew, Knudson, Grześ Marek, and SpringerLink (Online service), eds. Adaptive and Learning Agents: International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Taylor, Matthew E. Adaptive and Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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Mamei, Marco. Field-based coordination for pervasive multiagent systems. Berlin: Springer, 2005.

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Adaptive bidding in single sided auctions under uncertainty: An agent-based approach in market engineering. Basel: Birkhauser Verlag AG, 2007.

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International, Joint Conference on Artificial Intelligence (14th 1995 Montréal Québec). Adaption and learning in multi-agent systems: IJCAI '95 workshop, Montréal, Canada, August 21, 1995, proceedings. Berlin: Springer, 1996.

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A, Vouros George, ed. Organized adaption in multi-agent systems: First international workshop, OAMAS 2008, Estoril, Portugal, May 13, 2008 : revised and invited papers. Berlin: Springer-Verlag, 2009.

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Capitoli di libri sul tema "Artificial adaptive agents"

Bosse, Tibor, Charlotte Gerritsen, Jeroen de Man, and Suzanne Tolmeijer. "Adaptive Training for Aggression de-Escalation." In Artificial Life and Intelligent Agents, 80–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18084-7_7.

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Ishida, Takashi, Hiroshi Yokoi, and Yukinori Kakazu. "Emergence of Adaptive Society with Competitive Selfish Agents." In Advances in Artificial Life, 555–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48304-7_74.

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Boulehouache, Soufiane, Ramdane Maamri, and Zaidi Sahnoun. "Toward Component-Based Self-Adaptive Multi-Strategic Pedagogical Agents." In Artificial Intelligence XXXIV, 169–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71078-5_16.

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Red’ko, Vladimir G., Oleg P. Mosalov, and Danil V. Prokhorov. "Investigation of Evolving Populations of Adaptive Agents." In Artificial Neural Networks: Biological Inspirations – ICANN 2005, 337–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_53.

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Lopes Cardoso, Henrique, and Eugénio Oliveira. "Using and Evaluating Adaptive Agents for Electronic Commerce Negotiation." In Advances in Artificial Intelligence, 96–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44399-1_11.

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Damoulas, Theodoros, Ignasi Cos-Aguilera, Gillian M. Hayes, and Tim Taylor. "Valency for Adaptive Homeostatic Agents: Relating Evolution and Learning." In Advances in Artificial Life, 936–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553090_94.

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Ganon, Zohar, Alon Keinan, and Eytan Ruppin. "Evolutionary Network Minimization: Adaptive Implicit Pruning of Successful Agents." In Advances in Artificial Life, 319–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39432-7_34.

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Legéndi, Richárd, László Gulyás, Rajmund Bocsi, and Tamás Máhr. "Modeling Autonomous Adaptive Agents with Functional Language for Simulations." In Progress in Artificial Intelligence, 449–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04686-5_37.

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Pacheco, Jorge M., Tom Lenaerts, and Francisco C. Santos. "Evolution of Cooperation in a Population of Selfish Adaptive Agents." In Advances in Artificial Life, 535–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74913-4_54.

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Vilà, Xavier. "Adaptive Artificial Agents Play a Finitely Repeated Discrete Principal-Agent Game." In Lecture Notes in Economics and Mathematical Systems, 437–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03366-1_35.

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Atti di convegni sul tema "Artificial adaptive agents"

"Adaptive Agents for Cyber-Physical Systems." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004244402570262.

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Frasheri, Mirgita, Baran Cürüklü, and Mikael Ekstroem. "Towards Collaborative Adaptive Autonomous Agents." In 9th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006195500780087.

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Ranjbar Sahraei, Bijan, Alireza Nemati, Mehdi Farshchi, and Ali Meghdari. "Adaptive Fuzzy Sliding Mode Control Approach for Swarm Formation Control of Multi-Agent Systems." In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-25371.

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In this paper, an adaptive control scheme for multi-agent formation control is proposed. This control method is based on artificial potential functions integrated with adaptive fuzzy sliding mode control technique. We consider fully actuated mobile agents with completely unknown dynamics. An adaptive fuzzy logic system is used to approximate the unknown system dynamics. Sliding Mode Control (SMC) theory is used to force agents’ motion to obey the dynamics defined by the simple inter-agent artificial potential functions. Stability proof is given using Lyapunov functions, which shows the robustn

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"Self-adaptive Topology Neural Network for Online Incremental Learning." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004811500940101.

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"A Multi-demand Adaptive Bargaining based on Fuzzy Logic." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004907005770585.

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"Agent-based Simulations of Patterns for Self-adaptive Systems." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004925001900200.

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Benomrane, Souad, Zied Sellami, Mounir Ben Ayed, and Adel M. Alimi. "An Adaptive Multi-Agent System for Ontology Co-evolution." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005257602160221.

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"Self-adaptive Aided Decision-making - Application to Maritime Surveillance." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004260404190422.

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Noblega, Ashey, Aline Paes, and Esteban Clua. "Towards Adaptive Deep Reinforcement Game Balancing." In 11th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007395406930700.

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Conn, Marvin, and Darsana Josyula. "Adaptive Classifiers: Applied to Radio Waveforms." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009180609870994.

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Rapporti di organizzazioni sul tema "Artificial adaptive agents"

Bullard, James, and John Duffy. A Model of Learning and Emulation with Artificial Adaptive Agents. Federal Reserve Bank of St. Louis, 1994. http://dx.doi.org/10.20955/wp.1994.014.

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