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Journal articles on the topic 'Autonomous agents'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Christman, John. "Autonomous Agents." Journal of Philosophy 96, no. 2 (1999): 95–99. http://dx.doi.org/10.5840/jphil19999627.

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2

Kauffman, Stuart. "Molecular autonomous agents." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361, no. 1807 (May 7, 2003): 1089–99. http://dx.doi.org/10.1098/rsta.2003.1186.

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3

Maes, Pattie. "Designing autonomous agents." Robotics and Autonomous Systems 6, no. 1-2 (June 1990): 1–2. http://dx.doi.org/10.1016/s0921-8890(05)80024-7.

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4

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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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 about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach. It attempts to extract its main ideas, evaluates what contributions have been made so far, and identifies its current limitations and open problems.
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5

Valavanis, K. P. "Autonomous agents [Book Reviews]." IEEE Transactions on Robotics and Automation 15, no. 6 (December 1999): 1149. http://dx.doi.org/10.1109/tra.1999.817684.

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6

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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7

Cronin, A. J. "Allowing autonomous agents freedom." Journal of Medical Ethics 34, no. 3 (March 1, 2008): 129–32. http://dx.doi.org/10.1136/jme.2007.023580.

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8

Neemeh, Zachariah A., Christian Kronsted, Sean Kugele, and Stan Franklin. "Body Schema in Autonomous Agents." Journal of Artificial Intelligence and Consciousness 08, no. 01 (February 16, 2021): 113–45. http://dx.doi.org/10.1142/s2705078521500065.

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A body schema is an agent’s model of its own body that enables it to act on affordances in the environment. This paper presents a body schema system for the Learning Intelligent Decision Agent (LIDA) cognitive architecture. LIDA is a conceptual and computational implementation of Global Workspace Theory, also integrating other theories from neuroscience and psychology. This paper contends that the ‘body schema’ should be split into three separate functions based on the functional role of consciousness in Global Workspace Theory. There is (1) an online model of the agent’s effectors and effector variables (Current Body Schema), (2) a long-term, recognitional storage of embodied capacities for action and affordances (Habitual Body Schema), and (3) “dorsal” stream information feeding directly from early perception to sensorimotor processes (Online Body Schema). This paper then discusses how the LIDA model of the body schema explains several experiments in psychology and ethology.
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9

Tulli, Silvia. "Explainability in Autonomous Pedagogical Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13738–39. http://dx.doi.org/10.1609/aaai.v34i10.7141.

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The research presented herein addresses the topic of explainability in autonomous pedagogical agents. We will be investigating possible ways to explain the decision-making process of such pedagogical agents (which can be embodied as robots) with a focus on the effect of these explanations in concrete learning scenarios for children. The hypothesis is that the agents' explanations about their decision making will support mutual modeling and a better understanding of the learning tasks and how learners perceive them. The objective is to develop a computational model that will allow agents to express internal states and actions and adapt to the human expectations of cooperative behavior accordingly. In addition, we would like to provide a comprehensive taxonomy of both the desiderata and methods in the explainable AI research applied to children's learning scenarios.
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10

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

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11

Endriss, Ulle, Ann Nowé, Maria Gini, Victor Lesser, Michael Luck, Ana Paiva, and Jaime Sichman. "Autonomous agents and multiagent systems." AI Matters 7, no. 3 (September 2021): 29–37. http://dx.doi.org/10.1145/3511322.3511329.

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The 2021 edition of AAMAS, the International Conference on Autonomous Agents and Multiagent Systems, took place from the 3rd to 7th of May 2021 (aamas2021.soton.ac.uk). This year it was organized in the form of a virtual event and attracted over 1,000 registered participants. As every year, the conference featured an exciting programme of contributed talks, keynotes addresses, tutorials, affiliated workshops, a doctoral consortium, and more.
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12

Le Poidevin, Robin. "Autonomous Agents or God’s Automata?" Cogito 9, no. 1 (1995): 35–41. http://dx.doi.org/10.5840/cogito19959143.

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13

Card, Alexander. "Structure Editors and Autonomous Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (September 25, 2018): 288–89. http://dx.doi.org/10.1609/aiide.v14i1.13007.

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Rational agents are becoming prevalent in many domains, from data analysis to entertainment and games. The increased prevalence of agents has evolved new tools and techniques to work with and design new agents. One such technique is system simulation. Systems simulation is a technique an author can use to imitate tasks, processes, or systems, and in particular, agents. Systems simulation has a variety of uses, ranging from simulating ecological systems to entertainment, such as interactive narratives and digital games. However, many system simulators use specialized programming languages and require prior programming experience. This causes a disconnect between individuals with limited programming experience who wish to use the simulation tools, and the software itself. New users may find the specialized languages daunting, and the initial learning process too intense for the anticipated reward. This research strives to bridge the gap between system simulation tools and users with little to no programming experience. Future work includes a corpus of narrative and autonomous agent creation tools designed for users with little to no programming experience.
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14

Relyea, Robert, Darshan Bhanushali, Abhishek Vashist, Amlan Ganguly, Andres Kwasinski, Michael E. Kuhl, and Raymond Ptucha. "Multimodal Localization for Autonomous Agents." Electronic Imaging 2019, no. 7 (January 13, 2019): 451–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.7.iriacv-451.

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15

Hung, Jason C., Han Bin Chang, Hsuan Pu Chang, Yu Hsin Cheng, and Kuo Yen Lo. "Evolution of ubiquitous autonomous agents." International Journal of Ad Hoc and Ubiquitous Computing 4, no. 6 (2009): 334. http://dx.doi.org/10.1504/ijahuc.2009.028661.

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16

Scheier, Christian, and Dimitrios Lambrinos. "Adaptive classification in autonomous agents." Applied Artificial Intelligence 11, no. 2 (March 1997): 119–30. http://dx.doi.org/10.1080/088395197118271.

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17

FRANKLIN, STAN. "AUTONOMOUS AGENTS AS EMBODIED AI." Cybernetics and Systems 28, no. 6 (September 1997): 499–520. http://dx.doi.org/10.1080/019697297126029.

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18

Mario Veitl, Paolo Petta, Robert Sp. "AUTONOMOUS AGENTS IN USER INTERFACES." Cybernetics and Systems 30, no. 2 (February 1999): 169–77. http://dx.doi.org/10.1080/019697299125334.

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19

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

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.
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20

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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21

JOHNSON, W. LEWIS, and BARBARA HAYES-ROTH. "The First Autonomous Agents Conference." Knowledge Engineering Review 13, no. 2 (July 1998): 137–42. http://dx.doi.org/10.1017/s0269888998002021.

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The First International Conference on Autonomous Agents brought together researchers concerned with implementing systems that perceive and act in dynamic, unpredictable environments, that coordinate interoperation among complementary component capabilities, and that perform significant jobs with a high degree of autonomy.
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22

Chopra, Samir. "Rights for autonomous artificial agents?" Communications of the ACM 53, no. 8 (August 2010): 38–40. http://dx.doi.org/10.1145/1787234.1787248.

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23

Marley, A. A. J. "Measurement, Models, and Autonomous Agents." Psychological Science 3, no. 2 (March 1992): 93–96. http://dx.doi.org/10.1111/j.1467-9280.1992.tb00005.x.

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24

Spafford, Eugene H., and Diego Zamboni. "Intrusion detection using autonomous agents." Computer Networks 34, no. 4 (October 2000): 547–70. http://dx.doi.org/10.1016/s1389-1286(00)00136-5.

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25

Pfeifer, Rolf. "Cognition — perspectives from autonomous agents." Robotics and Autonomous Systems 15, no. 1-2 (July 1995): 47–70. http://dx.doi.org/10.1016/0921-8890(95)00014-7.

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26

ANDRECUT, M., and M. K. ALI. "SELF-ADAPTING REACTIVE AUTONOMOUS AGENTS." International Journal of Modern Physics B 14, no. 18 (July 20, 2000): 1915–26. http://dx.doi.org/10.1142/s0217979200002077.

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This paper describes a new self-adapting control algorithm for reactive autonomous agents. The architecture of the autonomous agents integrates the reactive behavior with reinforcement learning. We show how these components perform on-line adaptation of the autonomous agents to various complex navigation situations by constructing an internal model of the environment. Also, a discussion on cooperation and coordination of teams of agents is presented.
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27

Shneiderman, Ben. "Human Responsibility for Autonomous Agents." IEEE Intelligent Systems 22, no. 2 (March 2007): 60–61. http://dx.doi.org/10.1109/mis.2007.32.

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28

Bullot, Thomas, Dominique Gaïti, Leila Merghem-Boulahia, Guy Pujolle, and Hubert Zimmermann. "Autonomous agents for autonomic networks." annals of telecommunications - annales des télécommunications 61, no. 9-10 (October 2006): 1017–45. http://dx.doi.org/10.1007/bf03219879.

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29

Wood, Sharon. "Representation and purposeful autonomous agents." Robotics and Autonomous Systems 49, no. 1-2 (November 2004): 79–90. http://dx.doi.org/10.1016/j.robot.2004.07.018.

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30

Wood, Sharon. "Representation and purposeful autonomous agents." Robotics and Autonomous Systems 51, no. 2-3 (May 2005): 217–28. http://dx.doi.org/10.1016/j.robot.2005.02.003.

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31

Harter, D., and R. Kozma. "Chaotic Neurodynamics for Autonomous Agents." IEEE Transactions on Neural Networks 16, no. 3 (May 2005): 565–79. http://dx.doi.org/10.1109/tnn.2005.845086.

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32

MARCOUX, CHRISTOPHER S. "Autonomous Actors or Faithful Agents?" International Studies Review 9, no. 2 (June 2007): 262–64. http://dx.doi.org/10.1111/j.1468-2486.2007.00674.x.

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33

Fagernes, Siri, and Alva L. Couch. "Resource-sharing among autonomous agents." Service Oriented Computing and Applications 12, no. 3-4 (October 20, 2018): 317–31. http://dx.doi.org/10.1007/s11761-018-0244-2.

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34

Pitt, Jeremy, Matthew Anderton, and Jim Cunningham. "Normalized Interactions between autonomous agents." Computer Supported Cooperative Work (CSCW) 5, no. 2-3 (1996): 201–22. http://dx.doi.org/10.1007/bf00133656.

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35

Botchkaryov, A. "AUTONOMOUS DECENTRALIZED COMPUTER NETWORK MONITORING SYSTEM BASED ON SOFTWARE AGENTS." Computer systems and network 5, no. 1 (December 16, 2023): 1–7. http://dx.doi.org/10.23939/csn2023.01.001.

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36

Sai Vyshnavi, Koyya Doondy. "Integration of Blockchain, Internet of Things and AI." International Journal of Research in Science and Technology 12, no. 04 (2022): 31–36. http://dx.doi.org/10.37648/ijrst.v12i04.006.

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The next phase of digital transformation will be propelled by technologies like blockchain, the Internet of Things (IoT), and artificial intelligence (AI). In this paper, we suggest that the convergence of these technologies will make possible novel forms of enterprise. Future autonomous agents will function as autonomous profit centers that have a digital twin leveraging IoT, send and receive money leveraging blockchain technology, and autonomously make decisions as independent economic agents utilizing artificial intelligence and data analytics. Further, we suggest that this convergence will propel the creation of such autonomous business models and, by extension, the digital transformation of industrial conglomerates.
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37

DOVIER, AGOSTINO, ANDREA FORMISANO, and ENRICO PONTELLI. "Autonomous agents coordination: Action languages meet CLP() and Linda." Theory and Practice of Logic Programming 13, no. 2 (September 24, 2012): 149–73. http://dx.doi.org/10.1017/s1471068411000615.

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AbstractThe paper presents a knowledge representation formalism, in the form of a high-levelAction Description Language (ADL)for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining to a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog.
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38

BEKEY, GEORGE A. "On autonomous robots." Knowledge Engineering Review 13, no. 2 (July 1998): 143–46. http://dx.doi.org/10.1017/s0269888998002033.

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Autonomous robots are the intelligent agents par excellence. We frequently define a robot as a machine that senses, thinks and acts, i.e., an agent. They are distinguished from software agents in that robots are embodied agents, situated in the real world. As such, they are subject both to the joys and sorrows of the world. They can be touched and seen and heard (sometimes even smelled!), they have physical dimensions, and they can exert force on other objects. These objects can be like a ball in the RoboCup or Mirosot robot soccer games, they can be parts to be assembled, airplanes to be washed, carpets to be vacuumed, terrain to be traversed or cameras to be aimed. On the other hand, since robots are agents in the world they are also subject to its physical laws, they have mass and inertia, their moving parts encounter friction and hence heat, no two parts are precisely alike, measurements are corrupted by noise, and alas, parts break. Of course, robots also contain computers, and hence they are also subject to the slings and arrows of computer misfortunes, both in hardware and software. Finally, the world into which we place these robots keeps changing, it is non-stationary and unstructured, so that we cannot predict its features accurately in advance.
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39

Schaub Jr., Gary. "Controlling the Autonomous Warrior." Journal of International Humanitarian Legal Studies 10, no. 1 (June 9, 2019): 184–202. http://dx.doi.org/10.1163/18781527-01001007.

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The challenges posed by weapons with autonomous functions are not a tabula rasa. The capabilities of both State principals and military agents to control and channel violence for political purposes have improved across the centuries as technology has increased the range and lethality of weapons as well as the scope of warfare. The institutional relations between principals and agents have been adapted to account for, and take advantage of, these developments. Air forces encompass one realm where distance, speed, and lethality have been subjected to substantial and effective control. Air forces are also where systems with autonomous functionality will likely drive the most visible adaptation to command and control arrangements. This process will spread across other domains as States pursue institution-centric and agent-centric strategies to secure meaningful human control over artificial agents as they become increasingly capable of replacing human agents in military (and other) functions. Agent-centric approaches that consider emergent behaviour as akin to human judgment and institutional approaches that improve the ability to understand, interrogate, monitor, and audit the decisions and behaviour of artificial agents can together drive improvements in meaningful human control over warfare, just as previous adaptations have.
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40

Zurek, Tomasz, and Michail Mokkas. "Value-Based Reasoning in Autonomous Agents." International Journal of Computational Intelligence Systems 14, no. 1 (2021): 896. http://dx.doi.org/10.2991/ijcis.d.210203.001.

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41

Ekenberg, Love, and Mats Danielson. "Automatized Decision Making for Autonomous Agents." International Journal of Intelligent Mechatronics and Robotics 3, no. 3 (July 2013): 22–28. http://dx.doi.org/10.4018/ijimr.2013070102.

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Utility theory and the principle of maximising the expected utility have, within the multi-agent community, had a great influence on multi-agent based decision. Even though this principle is often useful when evaluating a decision situation it is virtually impossible, except in very artificial situations, to use the more basic decision rules with its unrealistically strong requirements for the input data, and other candidate methods must be considered instead. This article provides an overview and brings attention to some of the possibilities to utilize more elaborated decision methods, while still keeping the computational issues at a tractable level.
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42

Archibald, James K., Wynn C. Stirling, and Matthew S. Nokleby. "Socially Rational Models for Autonomous Agents." Open Cybernetics & Systemics Journal 2, no. 1 (June 3, 2008): 122–41. http://dx.doi.org/10.2174/1874110x00802010122.

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43

Valbahs, Edvards, and Peter Grabusts. "Path Planning Usage for Autonomous Agents." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 2 (August 8, 2015): 40. http://dx.doi.org/10.17770/etr2013vol2.867.

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In order to achieve the wide range of the robotic application it is necessary to provide iterative motions among points of the goals. For instance, in the industry mobile robots can replace any components between a storehouse and an assembly department. Ammunition replacement is widely used in military services. Working place is possible in ports, airports, waste site and etc. Mobile agents can be used for monitoring if it is necessary to observe control points in the secret place. The paper deals with path planning programme for mobile robots. The aim of the research paper is to analyse motion-planning algorithms that contain the design of modelling programme. The programme is needed as environment modelling to obtain the simulation data. The simulation data give the possibility to conduct the wide analyses for selected algorithm. Analysis means the simulation data interpretation and comparison with other data obtained using the motion-planning. The results of the careful analysis were considered for optimal path planning algorithms. The experimental evidence was proposed to demonstrate the effectiveness of the algorithm for steady covered space. The results described in this work can be extended in a number of directions, and applied to other algorithms.
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44

Yalovets, A. L. "About the Taxonomy of Autonomous Agents." Èlektronnoe modelirovanie 40, no. 1 (February 27, 2018): 3–30. http://dx.doi.org/10.15407/emodel.40.01.003.

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45

Zhao, Li, Duwu Cui, and Lei Wang. "Associative classification with evolutionary autonomous agents." International Journal of Modelling, Identification and Control 14, no. 4 (2011): 294. http://dx.doi.org/10.1504/ijmic.2011.043153.

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46

Lincoln, N. K., S. M. Veres, L. A. Dennis, M. Fisher, and A. Lisitsa. "Autonomous Asteroid Exploration by Rational Agents." IEEE Computational Intelligence Magazine 8, no. 4 (November 2013): 25–38. http://dx.doi.org/10.1109/mci.2013.2279559.

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47

Guessoum, Z., and J. P. Briot. "From active objects to autonomous agents." IEEE Concurrency 7, no. 3 (July 1999): 68–76. http://dx.doi.org/10.1109/4434.788781.

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48

Jennings, N. R., T. J. Norman, P. Faratin, P. O'Brien, and B. Odgers. "Autonomous agents for business process management." Applied Artificial Intelligence 14, no. 2 (February 2000): 145–89. http://dx.doi.org/10.1080/088395100117106.

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49

OGSTON, ELTH, MAARTEN VAN STEEN, and FRANCES BRAZIER. "GROUP FORMATION AMONG DECENTRALIZED AUTONOMOUS AGENTS." Applied Artificial Intelligence 18, no. 9-10 (October 2004): 953–70. http://dx.doi.org/10.1080/08839510490514869.

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50

Ruppin, Eytan. "Evolutionary autonomous agents: A neuroscience perspective." Nature Reviews Neuroscience 3, no. 2 (February 2002): 132–41. http://dx.doi.org/10.1038/nrn729.

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