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Relevant bibliographies by topics / Autonomous agents

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Academic literature on the topic 'Autonomous agents'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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Journal articles on the topic "Autonomous agents"

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

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

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Dissertations / Theses on the topic "Autonomous agents"

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Frasheri, Mirgita. "Collaborative Adaptive Autonomous Agents." Licentiate thesis, Mälardalens högskola, Inbyggda system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-40255.

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Grey, Stuart. "Distributed agents for autonomous spacecraft." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/3830/.

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Space missions have evolved considerably in the last fifty years in both complexity and ambition. In order to enable this continued improvement in the scientific and commercial return of space missions new control systems are needed that can manage complex combinations of state of the art hardware with a minimum of human interaction. Distributed multi-agent systems are one approach to controlling complex multisatellite space missions. A distributed system is not enough on its own however,the spacecraft must be able to carry out complex tasks such as planning,negotiation and close proximity formation flying autonomously. It is the coupling of distributed control with autonomy that is the focus of this thesis. Three contributions to the state of the art are described herein. They all involve the innovative use of multi-agent systems in space missions. The first is the development of a multi-agent architecture, HASA, specifically for space missions. The second is to use embedded agents to autonomously control an interferometric type space telescope. The third is based on software agents that coordinate multiple Earth observation missions coupled with a global optimisation technique for data extraction. The HASA architecture was developed in reaction to the over generality of most multi-agent architectures in the computer science and robotics literature and the ad-hoc, case-by-case approach, to multi-agent architectures when developed and deployed for space missions. The HASA architecture has a recursive nature which allows for the multi-agent system to be completely described throughout its development process as the design evolves and more sub-systems are implemented. It also inherits a focus on the robust generation of a product and safe operation from architectures in use in the manufacturing industry. A multi-agent system was designed using the HASA architecture for an interferometric space telescope type mission. This type of mission puts high requirements on formation flying and cooperation between agents. The formation flying agents were then implemented using a Java framework and tested on a multi-platform distributed simulation suite developed especially for this thesis. Three different control methods were incorporated into the agents and the multi-agent system was shown to be able to acquire and change formation and avoid collisions autonomously. A second multi-agent system was designed for the GMES mission in collaboration with GMV, the industrial partner in this project. This basic MAS design was transferred to the HASA architecture. A novel image selection algorithm was developed to work alongside the GMES multi-agent system. This algorithm uses global optimisation techniques to suggest image parameters to users based on the output of the multi-agent system.

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Allen, Stephen Richard. "Concern processing in autonomous agents." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369169.

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Griffiths, Nathan. "Motivated cooperation in autonomous agents." Thesis, University of Warwick, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365266.

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DaBose, Michael W. "Autonomous agents for digital network maximization." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA337729.

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Thesis (M.S. in Software Engineering)--Naval Postgraduate School, Sept. 1997.
Thesis advisors: Luqi ; Berzins, Valdis. "September 1997." Includes bibliographical references (p. 108-112). Also Available online.

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Karnan, Sathia Murthy. "Knowledge based communication in autonomous agents." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ56334.pdf.

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Kaptan, Varol. "MODELING AUTONOMOUS AGENTS IN MILITARY SIMULATIONS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3825.

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Simulation is an important tool for prediction and assessment of the behavior of complex systems and situations. The importance of simulation has increased tremendously during the last few decades, mainly because the rapid pace of development in the field of electronics has turned the computer from a costly and obscure piece of equipment to a cheap ubiquitous tool which is now an integral part of our daily lives. While such technological improvements make it easier to analyze well-understood deterministic systems, increase in speed and storage capacity alone are not enough when simulating situations where human beings and their behavior are an integral part of the system being studied. The problem with simulation of intelligent entities is that intelligence is still not well understood and it seems that the field of Artificial Intelligence (AI) has a long way to go before we get computers to think like humans. Behavior-based agent modeling has been proposed in mid-80's as one of the alternatives to the classical AI approach. While used mainly for the control of specialized robotic vehicles with very specific sensory capabilities and limited intelligence, we believe that a behavior-based approach to modeling generic autonomous agents in complex environments can provide promising results. To this end, we are investigating a behavior-based model for controlling groups of collaborating and competing agents in a geographic terrain. In this thesis, we are focusing on scenarios of military nature, where agents can move within the environment and adversaries can eliminate each other through use of weapons. Different aspects of agent behavior like navigation to a goal or staying in group formation, are implemented by distinct behavior modules and the final observed behavior for each agent is an emergent property of the combination of simple behaviors and their interaction with the environment. Our experiments show that while such an approach is quite efficient in terms of computational power, it has some major drawbacks. One of the problems is that reactive behavior-based navigation algorithms are not well suited for environments with complex mobility constraints where they tend to perform much worse than proper path planning. This problem represents an important research question, especially when it is considered that most of the modern military conflicts and operations occur in urban environments. One of the contributions of this thesis is a novel approach to reactive navigation where goals and terrain information are fused based on the idea of transforming a terrain with obstacles into a virtual obstacle-free terrain. Experimental results show that our approach can successfully combine the low run-time computational complexity of reactive methods with the high success rates of classical path planning. Another interesting research problem is how to deal with the unpredictable nature of emergent behavior. It is not uncommon to have situations where an outcome diverges significantly from the intended behavior of the agents due to highly complex nonlinear interactions with other agents or the environment itself. Chances of devising a formal way to predict and avoid such abnormalities are slim at best, mostly because such complex systems tend to be be chaotic in nature. Instead, we focus on detection of deviations through tracking group behavior which is a key component of the total situation awareness capability required by modern technology-oriented and network-centric warfare. We have designed a simple and efficient clustering algorithm for tracking of groups of agent suitable for both spatial and behavioral domain. We also show how to detect certain events of interest based on a temporal analysis of the evolution of discovered clusters.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science

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Szarowicz, Adam. "Artificial intelligence for animated autonomous agents." Thesis, Kingston University, 2004. http://eprints.kingston.ac.uk/20735/.

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Automatic creation of animated crowd scenes involving multiple interacting characters is currently a field of extensive research. This is because automatic generation of animation finds immediate applications in film post-production and special effects, computer games or event simulation in crowded areas. The work presented here addresses the problem of inadequate application of AI techniques in current animation research. The thesis presents a survey of different industrial and academic approaches and a number of lacking features are identified. After extensive research in existing systems an agent-based system and an animation framework are chosen for extension and the cognitive architecture FreeWill is proposed. The architecture further extends its underlying principles and combines software agent solutions with typical animation elements. It also allows for easy integration with existing tools. Another important contribution of FreeWill is a proposal of an algorithm for automatic generation of high-level actions using reinforcement learning. The chosen learning technique lends itself well to the animation task, as reinforcement learning allows for easy definition of the learning task - only the ultimate goal of the learning agent must be defined. The process of defining and conducting the learning task is explained in detail. The learning module allows for further automation of the process of animation generation, shortens the task of creating crowd scenes and also reduces the production costs. Newly learnt actions can be applied to increase the quality of the generated sequences. The learning module can be used in both deterministic and non-deterministic environments. Experiments in both modes are presented, and conclusions are drawn. Two modes of control - inverse and forward kinematics are also compared and a number of experiments are demonstrated. Learning with inverse kinematics control was found to converge faster for the same task. A working prototype of the architecture is presented and the learnt motion is compared with human motion. The results of the comparison demonstrate that the learning scheme could be used to imitate human motion in crowd scenes. Finally a number of metrics are defined which allow for easy selection of most relevant actions from the learnt set, again helping to automate the process. The work concludes with pointing out further directions of research based on this work and suggests possible extensions and applications.

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Coddington, Alexandra Margrit. "Self motivated planning in autonomous agents." Thesis, University College London (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249232.

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Vorrath, Jonathan J. "Implementing METOC transformation : applying autonomous agents /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FVorrath.pdf.

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Books on the topic "Autonomous agents"

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Bekey, George A., ed. Autonomous Agents. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7.

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1928-, Bekey George A., ed. Autonomous agents. Boston: Kluwer, 1998.

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Mukhopadhyay, Subhas Chandra, and Gourab Sen Gupta, eds. Autonomous Robots and Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73424-6.

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Osman, Nardine, and Carles Sierra, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46840-2.

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Schroeder, Michael. Autonomous, Model-Based Diagnosis Agents. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5739-5.

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von Martial, Frank. Coordinating Plans of Autonomous Agents. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/bfb0016366.

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Sukthankar, Gita, and Juan A. Rodriguez-Aguilar, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71679-4.

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Sukthankar, Gita, and Juan A. Rodriguez-Aguilar, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71682-4.

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Osman, Nardine, and Carles Sierra, eds. Autonomous Agents and Multiagent Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46882-2.

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Beaudoin, Luc. Goal processing in autonomous agents. Birmingham: University of Birmingham, 1994.

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Book chapters on the topic "Autonomous agents"

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Schroeder, Michael. "Autonomous Agents." In Autonomous, Model-Based Diagnosis Agents, 83–112. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5739-5_5.

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Stuart-Smith, Robert. "Autonomous Agents." In Behavioural Production, 62–96. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003028291-4.

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Bekey, George A. "Introduction." In Autonomous Agents, 5. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_1.

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Fujita, Masahiro, and Hiroaki Kitano. "Development of an Autonomous Quadruped Robot for Robot Entertainment." In Autonomous Agents, 7–18. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_2.

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Garcia-Alegre, Maria C., and Felicidad Recio. "Basic Visual and Motor Agents for Increasingly Complex Behavior Generation on a Mobile Robot." In Autonomous Agents, 19–28. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_3.

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Pell, Barney, Douglas E. Bernard, Steve A. Chien, Erann Gat, Nicola Muscettola, P. Pandurang Nayak, Michael D. Wagner, and Brian C. Williams. "An Autonomous Spacecraft Agent Prototype." In Autonomous Agents, 29–52. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_4.

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López-Sánchez, M., F. Esteva, R. López De Màntaras, C. Sierra, and J. Amat. "Map Generation by Cooperative Low-Cost Robots in Structured Unknown Environments." In Autonomous Agents, 53–61. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_5.

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Horswill, Ian Douglas. "Grounding Mundane Inference in Perception." In Autonomous Agents, 63–77. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_6.

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Haigh, Karen Zita, and Manuela M. Veloso. "Interleaving Planning and Robot Execution for Asynchronous User Requests." In Autonomous Agents, 79–95. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_7.

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Durfee, Edmund H., Patrick G. Kenny, and Karl C. Kluge. "Integrated Premission Planning and Execution for Unmanned Ground Vehicles." In Autonomous Agents, 97–110. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5735-7_8.

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Conference papers on the topic "Autonomous agents"

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Azaria, Amos. "Irrational, but Adaptive and Goal Oriented: Humans Interacting with Autonomous Agents." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/813.

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Autonomous agents that interact with humans are becoming more and more prominent. Currently, such agents usually take one of the following approaches for considering human behavior. Some methods assume either a fully cooperative or a zero-sum setting; these assumptions entail that the human's goals are either identical to that of the agent, or their opposite. In both cases, the agent is not required to explicitly model the human’s goals and account for humans' adaptation nature. Other methods first compose a model of human behavior based on observing human actions, and then optimize the agent’s actions based on this model. Such methods do not account for how the human will react to the agent's actions and thus, suffer an overestimation bias. Finally, other methods, such as model free reinforcement learning, merely learn which actions the agent should take at which states. While such methods can, theoretically, account for human adaptation nature, since they require extensive interaction with humans, they usually run in simulation. By not considering the human’s goals, autonomous agents act selfishly, lack generalization, require vast amounts of data, and cannot account for human’s strategic behavior. Therefore, we call for pursuing solution concepts for autonomous agents interacting with humans that consider the human’s goals and adaptive nature.

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Sudo, Yasuhiro, Keisuke Kasiwase, and Michiko Matsuda. "Verification of Scheduling Efficiency of an Autonomous Assembly System Using the Multi-Agent Manufacturing Simulator." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7231.

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This study is an examination of the effect of agent-based autonomous production scheduling, using the virtual factory on multi-agent simulation system. In the autonomous manufacturing system, a production plan is generated autonomously and dynamically, using communication and negotiation between agents that correspond to factory components. As infrastructure software for agent based manufacturing, the artisoc(c) is used as multi-agent simulator system. In this virtual factory, three types of agents are equipped. Users can operate a configuration such as input new jobs, adjusting a machine setting, etc, with monitoring conditions of agents. Additionally, this simulator has capability of input and output files such as assembly process schedules and logs of practical operations. As a result, by adjustment of the agent’s behavior with factory floor detail, the assembly schedule becomes more effective. The experiment was carried out to show that local negotiations contribute to global optimization when resources in the factory are effectively distributed and shared.

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Wang, Shilong, Jian Yi, Xia Hong, and Z. Zhang. "Heterogeneous Autonomous Agent Architecture for Agile Manufacturing." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/cie-34397.

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Considering the agent-based modeling and mapping in manufacturing system, some system models are described in this paper, which are included: Domain Based Hierarchical Structure (DBHS), Cascading Agent Structure (CAS), Proximity Relation structure (PRS), and Bus-based network structure (BNS). In DBHS, one sort of agents, called static agents, individually acts as Domain Agents, Resources Agents, UserInterface Agents and Gateway Agents. And the others, named mobile agents, are the brokers of task and process flow. Static agents representing a subsystem may itself be an agent-based network and should learn as the mobile agents to deal with new situation. Mobile agents move around the network domains taking advantage of the resources to fulfill their goals. In CAS, We use Unified Modeling Language (UML) to build up the agent-based manufacturing system It is said Enterprise agent (main agent) has factory agents together with some directly jurisdictional workshop agents, cell agents, and individual resource agents. Likewise, factory agent has workshop agents together with some directly jurisdictional cell agents and individual resource agents, and so on. In PRS, the resources agents are located together by its function and abilities. There is only one agent behaves as the task-announcer. The communication just occurs among the Proximity Relational agents. In BNS, It is very similar with the society of human being connected with a network, some agents, such as ‘cost calculating’, are just cope with the matter-of-fact job. And some agents run as the individual resources that can negotiate with each other and advertise a necessary message within the whole domain or a given group of agents. The administration just relies on the individual address of agents and the group ID code of agents.

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Cano Córdoba, Filip, Samuel Judson, Timos Antonopoulos, Katrine Bjørner, Nicholas Shoemaker, Scott J. Shapiro, Ruzica Piskac, and Bettina Könighofer. "Analyzing Intentional Behavior in Autonomous Agents under Uncertainty." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/42.

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Principled accountability for autonomous decision-making in uncertain environments requires distinguishing intentional outcomes from negligent designs from actual accidents. We propose analyzing the behavior of autonomous agents through a quantitative measure of the evidence of intentional behavior. We model an uncertain environment as a Markov Decision Process (MDP). For a given scenario, we rely on probabilistic model checking to compute the ability of the agent to influence reaching a certain event. We call this the scope of agency. We say that there is evidence of intentional behavior if the scope of agency is high and the decisions of the agent are close to being optimal for reaching the event. Our method applies counterfactual reasoning to automatically generate relevant scenarios that can be analyzed to increase the confidence of our assessment. In a case study, we show how our method can distinguish between 'intentional' and 'accidental' traffic collisions.

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Lieberman, Henry. "Autonomous interface agents." In the SIGCHI conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/258549.258592.

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Pschetz, L., and C. Speed. "Autonomous economic agents." In Living in the Internet of Things (IoT 2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.0146.

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"Autonomous Aquatic Agents." In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004220003720375.

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Minarsch, David, Seyed Ali Hosseini, Marco Favorito, and Jonathan Ward. "Trading Agent Competition with Autonomous Economic Agents." In Special Session on Super Distributed and Multi-agent Intelligent Systems. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010431805740582.

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Barber, K. S., R. M. McKay, C. E. Martin, T. H. Liu, J. Kim, D. Han, and A. Goel. "Sensible Agents in Supply Chain Management: An Example Highlighting Procurement and Production Decisions." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/cie-9078.

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Abstract Agent-based technologies can be applied to many aspects of supply chain management. The need for responsive, flexible agents is pervasive in this environment due to its complex, dynamic nature. Two critical aspects of agent capabilities are the ability to (1) classify agent behaviors according to autonomy level and (2) adapt problem-solving roles to various problem-solving situations during system operation. Sensible Agents, capable of Dynamic Adaptive Autonomy, have been developed to address these issues. A Sensible Agent’s “autonomy level” constitutes a description of the agent’s problem-solving role with respect to a particular goal. Problem-solving roles are defined along a spectrum of autonomy ranging from command-driven, to consensus, to locally autonomous/master. Dynamic Adaptive Autonomy is a capability that allows Sensible Agents to change autonomy levels during system operation to meet the needs of a particular problem-solving situation. This paper provides an overview of the Sensible Agent Testbed and introduces an example supply chain management domain with a scenario showing how this testbed could be used to simulate agent-based problem solving.

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Trujillo, Anna, Henry Fan, Lucas Hempley, Charles D. Cross, William L. Fehlman, Mark A. Motter, James H. Neilan, Garry Qualls, Paul M. Rothhaar, and Bonnie D. Allen. "Collaborating with Autonomous Agents." In 15th AIAA Aviation Technology, Integration, and Operations Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-3033.

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Reports on the topic "Autonomous agents"

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Langley, Pat, Kevin Thompson, Wayne Iba, John H. Gennari, and John A. Allen. An Integrated Cognitive Architecture for Autonomous Agents. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada225701.

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2

Lohn, Andrew, Anna Knack, Ant Burke, and Krystal Jackson. Autonomous Cyber Defense. Center for Security and Emerging Technology, June 2023. http://dx.doi.org/10.51593/2022ca007.

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Abstract:

The current AI-for-cybersecurity paradigm focuses on detection using automated tools, but it has largely neglected holistic autonomous cyber defense systems — ones that can act without human tasking. That is poised to change as tools are proliferating for training reinforcement learning-based AI agents to provide broader autonomous cybersecurity capabilities. The resulting agents are still rudimentary and publications are few, but the current barriers are surmountable and effective agents would be a substantial boon to society.

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Shen, Wei-Min. Self-Organizing and Autonomous Learning Agents and Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada430491.

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Durfee, Edmund H. Multilevel Coordination Mechanisms for Real-Time Autonomous Agents. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada421748.

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Voulgaris, Petros, Soon-Jo Chung, Seth Hutchinson, Steve LaValle, and Magnus Engestadt. Minimal Representation and Decision Making for Networked Autonomous Agents. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ad1001335.

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Pollack, Martha E. Plan Management Capabilities for Autonomous Agents: Extending the Basic Mechanisms. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada408497.

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Lockery, Shawn R. Noise-Tolerant Neural Networks Controlling Adaptive Behavior in Autonomous Agents. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada327443.

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Fink, Glenn A. Tactical Deployment and Management of Autonomous Agents, LDRD Final Report. Office of Scientific and Technical Information (OSTI), November 2007. http://dx.doi.org/10.2172/1025696.

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Dzenitis, J., B. Hindson, M. McBride, A. Makarewicz, B. Henderer, U. Sathyam, S. Smith, et al. Detection of aerosolized biological agents by immunoassay followed by autonomous PCR confirmation. Office of Scientific and Technical Information (OSTI), December 2003. http://dx.doi.org/10.2172/15013780.

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Koch, Joshua Bruce. Autonomous construction agents: An investigative framework for large sensor network self-management. Office of Scientific and Technical Information (OSTI), January 2008. http://dx.doi.org/10.2172/1342560.

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