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Academic literature on the topic 'Artificial autonomous agent'

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Published: 4 June 2025

Last updated: 6 July 2025

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Journal articles on the topic "Artificial autonomous agent"

Dong, Daqi. "The Observable Mind: Enabling an Autonomous Agent Sharing Its Conscious Contents Using a Cognitive Architecture." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 172–76. http://dx.doi.org/10.1609/aaaiss.v2i1.27666.

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We enable an autonomous agent sharing its artificial mind to its audiences like humans. This supports the autonomous human robot interactions relying on a cognitive architecture, LIDA, which explains and predicts how minds work and is used as the controllers of intelligent autonomous agents. We argue that LIDA’s cognitive representations and processes may serve as the source of the mind content its agent shares out, autonomously. We proposed a new description (sub) model into LIDA, letting its agent describing its conscious contents. Through this description, the agent’s mind is more observable so we can understand the agent’s entity and intelligence more directly. Also, this helps the agent explains its behaviors to its audiences so engage into its living society better. We built an initial LIDA agent embedding with this description model. The agent shares its conscious content autonomously, reasonably explaining its behaviors.

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Dodig-Crnkovic, Gordana, and Mark Burgin. "A Systematic Approach to Autonomous Agents." Philosophies 9, no. 2 (2024): 44. http://dx.doi.org/10.3390/philosophies9020044.

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Agents and agent-based systems are becoming essential in the development of various fields, such as artificial intelligence, ubiquitous computing, ambient intelligence, autonomous computing, and intelligent robotics. The concept of autonomous agents, inspired by the observed agency in living systems, is also central to current theories on the origin, development, and evolution of life. Therefore, it is crucial to develop an accurate understanding of agents and the concept of agency. This paper begins by discussing the role of agency in natural systems as an inspiration and motivation for agential technologies and then introduces the idea of artificial agents. A systematic approach is presented for the classification of artificial agents. This classification aids in understanding the existing state of the artificial agents and projects their potential future roles in addressing specific types of problems with dedicated agent types.

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Pooja, Dr. Manish Varshney. "The Study of Fundamental Concepts of Agent and Multi-agent Systems." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 3237–38. http://dx.doi.org/10.52783/tjjpt.v44.i3.1592.

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The concept of n intelligent agent is a concept that is born from the area of artificial intelligence; in fact, a commonly-accepted definition relates the discipline of artificial intelligence with the analysis and design of autonomous entities capable of exhibitin intelligent behavior. From that perspective, it is assumed that an intelligent agent must be able to perceive its environment, reason about how to achieve its objectives, act towards achieving them through the application of some principle of rationality, and interact with other intelligent agents, being artificial or human [1]. Multi-agent systems are a particular case of a distributed system, and its particularity lies in the fact that the components of the system are autonomous and selfish, seeking to satisfy their own objectives. In addition, these systems also stand out for being open systems without a centralized design [2]. One main reason for the great interest and attention that multi-agent systems have received is that they are seen as an enabling technology for complex applications that require distributed and parallel processing of data and operate autonomously in complex and dynamic domains.

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Rao, Swaneet D. "Multi-Agent Autonomous Cleaning." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1872–75. http://dx.doi.org/10.22214/ijraset.2021.38714.

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Abstract: In today’s world, robots are taking over the world by doing the tasks which used to be done by humans a while ago. Robots are continuously evolving into better and more efficient autonomous agents, makes substantial growth in fields like adaptive artificial intelligence. Our main objective of this people is to create an efficient multi agent autonomous environment for robots for cleaning purposes. Keywords: Gradient Descent, Centralized controller, autonomous agents, LiDAR

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Schaub Jr., Gary. "Controlling the Autonomous Warrior." Journal of International Humanitarian Legal Studies 10, no. 1 (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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Such, Jose M., Agustín Espinosa, and Ana García-Fornes. "A survey of privacy in multi-agent systems." Knowledge Engineering Review 29, no. 3 (2013): 314–44. http://dx.doi.org/10.1017/s0269888913000180.

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AbstractPrivacy has been a concern for humans long before the explosive growth of the Internet. The advances in information technologies have further increased these concerns. This is because the increasing power and sophistication of computer applications offers both tremendous opportunities for individuals, but also significant threats to personal privacy. Autonomous agents and multi-agent systems are examples of the level of sophistication of computer applications. Autonomous agents usually encapsulate personal information describing their principals, and therefore they play a crucial role in preserving privacy. Moreover, autonomous agents themselves can be used to increase the privacy of computer applications by taking advantage of the intrinsic features they provide, such as artificial intelligence, pro-activeness, autonomy, and the like. This article introduces the problem of preserving privacy in computer applications and its relation to autonomous agents and multi-agent systems. It also surveys privacy-related studies in the field of multi-agent systems and identifies open challenges to be addressed by future research.

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Wang, Jiasheng, and Aziz Nazha. "Autonomous Analysis of CIBMTR Datasets Using Artificial Intelligence Agents." Blood 144, Supplement 1 (2024): 7489. https://doi.org/10.1182/blood-2024-207380.

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Background: Analyzing complex medical data requires specialized knowledge and expertise, making it both time-consuming and resource-intensive. Large language models (LLMs), such as GPT-4, excel in tasks like coding and medical statistics. However, analyzing datasets is more intricate than interacting with a chatbot. It involves several critical steps: planning, tracking information, locating data, and developing and refining the right statistical analyses. Artificial intelligence (AI) agents represent a new trend, where each AI agent can perform specific tasks based on prior defined instructions. We have developed a framework in which multiple AI agents collaborate to accomplish a specific task. In this framework, each agent is assigned a distinct role in the data analysis process. They communicate by sending and receiving messages to coordinate their efforts, ensuring the task is completed in a systematic yet collaborative manner. Methods: We included the latest 20 studies from the Center for International Blood and Marrow Transplant Research (CIBMTR) with publicly available data. The primary objective was to evaluate the accuracy of AI agents in replicating the primary outcomes of these studies. Using the AutoGen platform, a six-party AI agent framework was constructed. This framework included a user proxy, planner, data retriever, data cleaner, coder, and results reviewer, with GPT-4o serving as the underlying LLM. It was given simple instructions to replicate the primary outcomes, such as “compare overall survival based on different reduced-intensity conditioning regimens.” The results were then compared to the original studies. Each experiment was repeated three times to assure accuracy. The included studies, instructions, and results can be viewed at https://github.com/jwang-580/CIBMTR_data. Results: The 20 included studies were published between 2021 and 2023, with topics spanning chronic leukemias (20%), health disparities (15%), immunobiology (15%), acute leukemias (10%), lymphomas (10%), graft-versus-host disease (10%), infection (10%), and survivorship (10%). The primary study objectives were either related to survival outcomes (75%) or specific complications (25%), such as the incidence of bacterial and viral infections (5%), pulmonary toxicities (5%), primary graft failure (5%), and secondary malignancies (5%). The statistical methods used for generating primary outcomes were multivariable regression analysis (45%), descriptive statistics (40%), and univariate regression analysis (15%). The AI agents successfully adhered to their designated roles by automatically downloading datasets and data dictionaries, drafting data analysis plans, selecting relevant variables, cleaning datasets, generating and debugging computational analysis codes, and interpreting the results. The multi-AI agent framework accurately replicated 53% of the primary outcomes (95% confidence interval [CI] 41-66%). This rate was significantly higher than that achieved using ChatGPT alone without the multi-AI agent framework, which replicated 35% of the results (95% CI 24-47%; p=0.04, t-test). Specifically, the multi-AI agent framework correctly replicated 58% of primary outcomes related to survival and 40% related to complications. It also successfully replicated 44%, 71%, and 67% of results from studies using multivariable regression, descriptive statistics, and univariate regression, respectively. The most common cause of failure to achieve accurate results was issues related to data transformation, such as converting time units or selecting data subsets. Notably, hallucination of data or results was not observed due to framework optimizations. The average cost of each analysis, which includes the expenses for processing input and output was $1.2, and each analysis was completed in &lt; 1 minute. Conclusion: We developed a multi-AI agent framework capable of collaboratively extracting, cleaning, organizing, and analyzing CIBMTR datasets. The agents successfully replicated published results efficiently and cost-effectively. Our approach significantly improves accuracy over the state-of-the-art, non-agent-based AI methods. It has the potential to transform complex data analysis, facilitating major advancements in medical research.

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Contreras, Daniel Silva, and Salvador Godoy-Calderon. "Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics." Applied Sciences 14, no. 8 (2024): 3303. http://dx.doi.org/10.3390/app14083303.

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When autonomous agents are deployed in an unknown environment, obstacle-avoiding movement and navigation are required basic skills, all the more so when agents are limited by partial-observability constraints. This paper addresses the problem of autonomous agent navigation under partial-observability constraints by using a novel approach: Artificial Potential Fields (APF) assisted by heuristics. The well-known problem of local minima is addressed by providing the agents with the ability to make individual choices that can be exploited in a swarm. We propose a new potential function, which provides precise control of the potential field’s reach and intensity, and the use of auxiliary heuristics provides temporary target points while the agent explores, in search of the position of the real intended target. Artificial Potential Fields, together with auxiliary search heuristics, are integrated into a novel navigation model for autonomous agents who have limited or no knowledge of their environment. Experimental results are shown in 2D scenarios that pose challenging situations with multiple obstacles, local minima conditions and partial-observability constraints, clearly showing that an agent driven using the proposed model is capable of completing the navigation task, even under the partial-observability constraints.

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Shinde, Dr Pravin, Himali Paradkar, Poojan Vig, Sanchay Thalnerkar, and Vinay Jain. "HELIX: Autonomous AI Agent." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4647–54. http://dx.doi.org/10.22214/ijraset.2024.61080.

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Abstract: Artificial Intelligence has transformed the way we interact with technology, introducing us to agents that can think and make choices like humans. At the heart of this evolution is our project, 'HELIX'. Through 'HELIX', we've developed AI agents specialized in a variety of tasks: from digging deep into the web for research, streamlining email communication through automation, efficiently sending out emails in bulk, to strategically identifying and generating potential business leads. By weaving together cutting-edge machine learning algorithms and advanced language models, our system stands as a testament to the proficiency and versatility of AI. What makes 'HELIX' especially groundbreaking is its user-friendly approach, ensuring anyone, regardless of their technical background, can harness its power. As we embrace the dawn of this technological era, 'HELIX' not only showcases the potential of today's AI capabilities but also paves the way for future innovations, promising an expansive horizon for AI-driven solutions across myriad domains.

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Montagna, Sara, Daniel Castro Silva, Pedro Henriques Abreu, Marcia Ito, Michael Ignaz Schumacher, and Eloisa Vargiu. "Autonomous agents and multi-agent systems applied in healthcare." Artificial Intelligence in Medicine 96 (May 2019): 142–44. http://dx.doi.org/10.1016/j.artmed.2019.02.007.

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Dissertations / Theses on the topic "Artificial autonomous agent"

Innocenti, Bianca. "A Multi-agent Architecture with Distributed Coordination for an Autonomous Robot." Doctoral thesis, Universitat de Girona, 2008. http://hdl.handle.net/10803/7749.

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Aquest treball proposa una nova arquitectura de control amb coordinació distribuïda per a un robot mòbil (ARMADiCo). La metodologia de coordinació distribuïda consisteix en dos passos: el primer determina quin és l'agent que guanya el recurs basat en el càlcul privat de la utilitat i el segon, com es fa el canvi del recurs per evitar comportaments abruptes del robot.<br/>Aquesta arquitectura ha estat concebuda per facilitar la introducció de nous components hardware i software, definint un patró de disseny d'agents que captura les característiques comunes dels agents. Aquest patró ha portat al desenvolupament d'una arquitectura modular dins l'agent que permet la separació dels diferents mètodes utilitzats per aconseguir els objectius, la col·laboració, la competició i la coordinació de recursos.<br/>ARMADiCo s'ha provat en un robot Pioneer 2DX de MobileRobots Inc.. S'han fet diversos experiments i els resultats han demostrat que s'han aconseguit les característiques proposades per l'arquitectura.<br>This work proposes a new mobile robot control architecture with distributed coordination (ARMADiCo). The distributed coordination methodology consists of two steps: the first determines which agent wins the resource based on a private utility computation, and the second how the resource exchange is carried out in order to avoid abrupt robot behaviors. <br/>This architecture has been conceived to make easy the introduction of new hardware and software components and an agent design pattern has been defined in order to capture the agents' common features. This pattern has also lead to the development of a modular architecture inside the agent that allows separation of the different methods of achieving goals, collaboration and competition as well as resource coordination.<br/>ARMADiCo has been tested on a MobileRobots Inc. Pioneer 2DX robot. Several experiments have been carried out and the results show that the proposed features of the architecture have been accomplished.

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Chrysanthakopoulos, Georgios. "A fuzzy-logic autonomous agent, applied as a supervisory controller in a simulated environment /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6044.

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Kumar, Sanjeev. "A formal semantics of teamwork and multi-agent conversations as the basis of a language for programming teams of autonomous agents /." Full text open access at:, 2006. http://content.ohsu.edu/u?/etd,17.

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Akbori, Fahrettin. "Autonomous-agent based simulation of anit-submarine warfare operations with the goal of protecting a high value unit /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FAkbori.pdf.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, March 2004.<br>Thesis advisor(s): Christian Darken, Curtis Blais. Includes bibliographical references (p. 103-104). Also available online.

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Heyder, Jakob. "Hierarchical Temporal Memory Software Agent : In the light of general artificial intelligence criteria." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75868.

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Artificial general intelligence is not well defined, but attempts such as the recent listof “Ingredients for building machines that think and learn like humans” are a startingpoint for building a system considered as such [1]. Numenta is attempting to lead thenew era of machine intelligence with their research to re-engineer principles of theneocortex. It is to be explored how the ingredients are in line with the design princi-ples of their algorithms. Inspired by Deep Minds commentary about an autonomy-ingredient, this project created a combination of Numentas Hierarchical TemporalMemory theory and Temporal Difference learning to solve simple tasks defined in abrowser environment. An open source software, based on Numentas intelligent com-puting platform NUPIC and Open AIs framework Universe, was developed to allowfurther research of HTM based agents on customized browser tasks. The analysisand evaluation of the results show that the agent is capable of learning simple tasksand there is potential for generalization inherent to sparse representations. However,they also reveal the infancy of the algorithms, not capable of learning dynamic com-plex problems, and that much future research is needed to explore if they can createscalable solutions towards a more general intelligent system.

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Akbori, Fahrettin. "Autonomous-agent based simulation of anti-submarine warfare operations with the goal of protecting a high value unit." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1718.

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Approved for public release, distribution unlimited<br>The Anti-Submarine Warfare screen design simulation is a program that provides a model for operations in anti-submarine warfare (ASW). The purpose of the program is to aid ASW commanders, allowing them to configure an ASW screen, including the sonar policy, convoy speed, and the number of ships, to gain insight into how these and other factors beyond their control, such as water conditions, impact ASW effectiveness. It is also designed to be used as a training tool for ASW officers. The program is implemented in Java programming language, using the Multi Agent System (MAS) technique. The simulation interface is a Horizontal Display Center (HDC) which is very similar to a MEKO200 class Frigate Combat Information Center's (CIC) HDC. The program uses Extensible Markup Language (XML) files for reading data for program scenarios; parameters are initialized before each run time begins. The simulation also provides all the output data at the end of run time for analysis purposes. The program user's goal, and the purpose of the program, is to decrease the number of successful attacks against surface vessels by changing the configuration parameters of the ASW screen, to reflect sonar policy, convoy speed or number of ships in the simulation. Ongoing use of the program can provide data needed to anticipate required operational needs in future ASW situations.<br>Lieutenant Junior Grade, Turkish Navy

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Colon, Matthew J. "Controlling the Uncontrollable: A New Approach to Digital Storytelling using Autonomous Virtual Actors and Environmental Manipulation." DigitalCommons@CalPoly, 2010. https://digitalcommons.calpoly.edu/theses/261.

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In most video games today that focus on a single story, scripting languages are used for controlling the artificial intelligence of the virtual actors. While scripting is a great tool for reliably performing a story, it has many disadvantages; mainly, it is limited by only being able to respond to those situations that were explicitly declared, causing unreliable responses to unknown situations, and the believability of the virtual actor is hindered by possible conflicts between scripted actions and appropriate responses as perceived by the viewer. This paper presents a novel method of storytelling by manipulating the environment, whether physically or the agent's perception of it, around the goals and behaviors of the virtual actor in order to advance the story rather than controlling the virtual actor explicitly. The virtual actor in this method is completely autonomous and the environment is manipulated by a story manager so that the virtual actor chooses to satisfy its goals in accordance with the direction of the story. Comparisons are made between scripting, traditional autonomy, Lionhead Studio's Black & White, Mateas and Stern's Façade, and autonomy with environmental manipulation in terms of design, performance, believability, and reusability. It was concluded that molding an environment around a virtual actor with the help of a story manager gives the actor the ability to reliably perform both event-based stories while preserving the believability and reusability of the actor and environment. While autonomous actors have traditionally been used solely for emergent storytelling, this new storytelling method enables them to be used reliably and efficiently to tell event-based stories as well while reaping the benefits of their autonomous nature. In addition, the separation of the virtual actors from the environment and story manager in terms of design promotes a cleaner, reusable architecture that also allows for independent development and improvement. By modeling artificial intelligence design after Herbert Simon's “artifact,” emphasizing the encapsulation of the inner mechanisms of virtual actors, the next era of digital storytelling can be driven by the design and development of reusable storytelling components and the interaction between the virtual actor and its environment.

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Pinto, Hugo da Silva Corrêa. "Designing autonomous agents for computer games with extended behavior networks : an investigation of agent performance, character modeling and action selection in unreal tournament." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2005. http://hdl.handle.net/10183/14784.

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Este trabalho investiga a aplicação de rede de comportamentos estendidas ao domínio de jogos de computador. Redes de comportamentos estendidas (RCE) são uma classe de arquiteturas para seleção de ações capazes de selecionar bons conjuntos de ações para agentes complexos situados em ambientes contínuos e dinâmicos. Foram aplicadas com sucesso na Robocup, mas nunca foram aplicadas a jogos. PHISH-Nets, um modelo de redes de comportamentos capaz de selecionar apenas uma ação por vez, foi aplicado à modelagem de personagens, com bons resultados. Apesar de RCEs serem aplicáveis a um conjunto de domínios maior, nunca foram usadas para modelagem de personagens. Apresenta-se como projetar um agente controlado por uma rede de comportamentos para o domínio do Unreal Tournament e como integrar a rede de comportamentos a sensores nebulosos e comportamentos baseados em máquinas de estado-finito aumentadas. Investiga-se a qualidade da seleção de ações e a correção do mecanismo em uma série de experimentos. A performance é medida através da comparação das pontuações de um agente baseado em redes de comportamentos com outros dois agentes. Um dos agentes foi implementado por outro grupo e usava sensores, efetores e comportamentos diferentes. O outro agente era idêntico ao agente baseado em RCEs, exceto pelo mecanismo de controle empregado. A modelagem de personalidade é investigada através do projeto e análise de cinco estereótipos: Samurai, Veterano, Berserker, Novato e Covarde. Apresenta-se três maneiras de construir personalidades e situa-se este trabalho dentro de outras abordagems de projeto de personalidades. Conclui-se que a rede de comportamentos estendida é um bom mecanismo de seleção de ações para o domínio de jogos de computador e um mecanismo interessante para a construção de agentes com personalidades simples.<br>This work investigates the application of extended behavior networks to the computer game domain. We use as our test bed the game Unreal Tournament. Extended Behavior Networks (EBNs) are a class of action selection architectures capable of selecting a good set of actions for complex agents situated in continuous and dynamic environments. They have been successfully applied to the Robocup, but never before used in computer games. PHISH-Nets, a behavior network model capable of selecting just single actions, was applied to character modeling with promising results. Although extended behavior networks are applicable to a larger domain, they had not been used to character modeling before. We present how to design an agent with extended behavior networks, fuzzy sensors and finite-state machine based behaviors. We investigate the quality of the action selection mechanism and its correctness in a series of experiments. The performance is assessed comparing the scores of an agent using an extended behavior network against a plain reactive agent with identical sensory-motor apparatus and against a totally different agent built around finite-state machines. We investigate how EBNs fare on agent personality modeling via the design and analysis of five stereotypes in Unreal Tournament. We discuss three ways to build character personas and situate our work within other approaches. We conclude that extended behavior networks are a good action selection architecture for the computer game domain and an interesting mechanism to build agents with simple personalities.

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Calfee, Sharif H. "Autonomous agent-based simulation of an AEGIS Cruiser combat information center performing battle air-defense commander operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FCalfee.pdf.

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Tlig, Mohamed. "Coordination locale et optimisation distribuée du trafic de véhicules autonomes dans un réseau routier." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0049/document.

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Dans le cadre de cette thèse, nous nous intéressons à la coordination et l'optimisation du trafic aux intersections des réseaux routiers, avec la particularité de considérer des véhicules autonomes intelligents. Cette thèse est organisée en deux grandes parties. La première se concentre sur le problème du partage d'un espace de voie par deux files de véhicules évoluant en sens opposés. L'état de l'art montre le peu de travaux abordant cette question. Nous explorons deux approches par coordination réactive, en relation avec un critère de minimisation des retards. Les performances de ces approches ont été mesurées statistiquement en simulation. La deuxième partie de la thèse s'attaque au problème générique de la gestion du trafic au sein d'un réseau routier. Nous développons une approche originale à deux égards: d'une part elle explore un principe de passage en alternance des flux permettant de ne pas arrêter les véhicules aux intersections, et d'autre part, elle propose des algorithmes d'optimisationdistribuée de ce passage alterné au niveau de chaque intersection et au niveau du réseau global. La thèse présente successivement les choix de modélisation, les algorithmes et l'étude en simulation de leurs performances comparées à desapproches existantes<br>In this thesis, we focus on traffic coordination and optimization in road intersections, while accounting for intelligent autonomous vehicles. This thesis is organized in two parts. The first part focuses on the problem of sharing a one-lane road between two opposite flows of vehicles. The state of the art shows few studies addressing this issue. We propose two reactive coordination approaches that minimize vehicle delays and measure their performances statistically through simulations. The second part of the thesis addresses the problem of generic traffic management in a traffic network. We develop a stop-free approach that explores a principle alternating vehicles between flows at intersections, and it provides distributed algorithms optimizing this alternation at each intersection and in the overall network. We present the modeling choices, the algorithms and the simulation study of our approach and we compare its performances with existing approaches

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Books on the topic "Artificial autonomous agent"

Pěchouček, Michal. Defence industry applications of autonomous agents and multi-agent systems. Birkhäuser, 2008.

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European, Workshop on Modelling Autonomous Agents in a. Multi-Agent World (3rd 1991 Kaiserslautern Germany). Decentralized A.I. 3: Proceedings of the Third European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Kaiserslautern, Germany, August 5-7, 1991. North-Holland, 1992.

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New York) International Joint Conference on Autonomous Agents and Multiagent Systems (3rd 2004 New York. AAMAS 2004: The third International Joint Conference on Autonomous Agents and Multi Agent Systems, New York, New York, USA. ACM Press, 2004.

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European Workshop on Modelling Autonomous Agents in a Multi-Agent World (1st 1989 Cambridge, England). Decentralized A.I.: Proceedings of the First European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Cambridge, England, August 16-18, 1989. North-Holland, 1990.

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European Workshop on Modelling Autonomous Agents in a Multi-Agent World (7th 1996 Eindhoven, Netherlands). Agents breaking away: 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW '96, Eindhoven, Netherlands, January 22-25, 1996 : proceedings. Springer, 1996.

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European Workshop on Modelling Autonomous Agents in a Multi-Agent World (6th 1994 Odense, Denmark). Distributed software agents and applications: 6th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'94, Odense, Denmark, August 3-5, 1994 : proceedings. Springer, 1996.

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European Workshop on Modelling Autonomous Agents in a Multi-Agent World (2nd 1990 Saint-Quentin-en-Yvelines, France). Decentralized A.I. 2: Proceedings of the Second European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Saint Quentin en Yvelines, France, August 13-16, 1990. North-Holland, 1991.

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Cristiano, Castelfranchi, and Werner Eric 1944-, eds. Artificial social systems: 4th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW '92, S. Martino al Cimino, Italy, July 29-31, 1992 : selected papers. Springer-Verlag, 1994.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics, and Cybernetics. (10th : 1998 : Baden-Baden, Germany), eds. Advances in artificial intelligence and engineering cybernetics: Neural networks, anticipatory systems, the evolution of autonomous agents, multi-agent systems development, intelligent systems in process control, knowledge organization, formal representation of meaning, space-time logic, logic networks, time and threshold dependent logic operators, natural language processing. International Institute for Advanced Studies in Systems Research and Cybernetics, 1999.

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1966-, White Laurence F., ed. A legal theory for autonomous artificial agents. University of Michigan Press, 2011.

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Book chapters on the topic "Artificial autonomous agent"

Red’ko, Vladimir G. "Towards Constructing an Autonomous Agent-Scientist." In Advances in Cognitive Research, Artificial Intelligence and Neuroinformatics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71637-0_75.

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Bhalla, Sushrut, Sriram Ganapathi Subramanian, and Mark Crowley. "Deep Multi Agent Reinforcement Learning for Autonomous Driving." In Advances in Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_7.

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de Sá, Claudio Cesar, Guilherme Bittencourt, and Nizam Omar. "An Autonomous Agent Architecture and the Locomotion Problem." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/10692710_2.

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Moriyama, Koichi, and Masayuki Numao. "Constructing an Autonomous Agent with an Interdependent Heuristics." In PRICAI 2000 Topics in Artificial Intelligence. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44533-1_35.

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Rault, Raphaël, and Damien Trentesaux. "Artificial Intelligence, Autonomous Systems and Robotics: Legal Innovations." In Service Orientation in Holonic and Multi-Agent Manufacturing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73751-5_1.

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Scheutz, Matthias. "The Need for Moral Competency in Autonomous Agent Architectures." In Fundamental Issues of Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26485-1_30.

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Monekosso, N., and P. Remagnino. "Autonomous Spacecraft Resource Management: A Multi-Agent Approach." In AI*IA 99: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46238-4_26.

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Markova, Vanya. "Simulation of the Autonomous Agent Behavior by Autoregressive Models." In Artificial Intelligence: Methodology, Systems, and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15431-7_35.

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Casas, Feliciano Manzano, and L. A. García. "OCOA: An Open, Modular, Ontology Based Autonomous Robotic Agent Architecture." In Artificial Intelligence: Methodology, Systems, and Applications. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46148-5_18.

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Davidsson, Paul. "A linearly quasi-anticipatory autonomous agent architecture: Some preliminary experiments." In Distributed Artificial Intelligence Architecture and Modelling. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61314-5_30.

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Conference papers on the topic "Artificial autonomous agent"

Li, Lu, Yuli Lu, Li Liu, and Yanhua Gao. "Design and Implementation of Collaborative Learning Algorithm for Vocational Education Based on Multi Agent System." In 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS). IEEE, 2024. http://dx.doi.org/10.1109/aiars63200.2024.00080.

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Wang, Yong, Zhicheng Tang, Daifeng Zhang, et al. "Generating Autonomous Driving Hazard Test Scenarios Using Multi-Agent Proximal Policy Optimization and Enhanced Artificial Potential Field Method." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662128.

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Wang, Caroline. "N-Agent Ad Hoc Teamwork." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/974.

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Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In fully cooperative multi-agent reinforcement learning, the learning algorithm controls all agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a single agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars to cooperate with each other, yet once on the road, these cars must additionally cooperate with cars from other companies. Towards expanding the class of scenarios that cooperative learning methods may optimally address, this research agenda introduces and proposes to study N-agent ad hoc teamwork (NAHT), where a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates.

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Wachi, Akifumi. "Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/832.

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We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent. Rule-based algorithms are required in safety-critical applications for them to work properly in a wide range of situations. Hence, every effort is made to find failure scenarios during the development phase. However, as the software becomes complicated, finding failure cases becomes difficult. Especially in multi-agent domains, such as autonomous driving environments, it is much harder to find useful failure scenarios that help us improve the algorithm. We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails. We demonstrate the effectiveness of our proposed method using a simple environment and autonomous driving simulator.

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Facchini, Sante Dino. "Decentralized Autonomous Organizations and Multi-agent Systems for Artificial Intelligence Applications and Data Analysis." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/828.

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The Ph.D research project aims to explore the potential of the Decentralized Autonomous Organization paradigm in conjunction with classic software architectures for Artificial Intelligence applications. The intended goal is to investigate and formalize a possible integration path between Multi-agent System architectures and Decentralized Autonomous Organizations. Starting from the Foundation for Intelligent Physical Agents standards, we will extend basic primitives to integrate Multi-agent Systems on Distributed Ledger Technology networks. Possible deployment of services and applications in the Internet-of-Things, Artificial Intelligence and Distributed Machine Learning areas will be tested. Application of Data Analysis techniques on datasets built on such a framework will be also addressed.

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Parker, Matt, and Gary B. Parker. "The Core: Evolving Autonomous Agent Control." In 2007 IEEE Symposium on Artificial Life. IEEE, 2007. http://dx.doi.org/10.1109/alife.2007.367800.

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Franklin, Matija, Hal Ashton, Edmond Awad, and David Lagnado. "Causal Framework of Artificial Autonomous Agent Responsibility." In AIES '22: AAAI/ACM Conference on AI, Ethics, and Society. ACM, 2022. http://dx.doi.org/10.1145/3514094.3534140.

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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}. 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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Narvekar, Sanmit, Jivko Sinapov, and Peter Stone. "Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/353.

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Transfer learning is a method where an agent reuses knowledge learned in a source task to improve learning on a target task. Recent work has shown that transfer learning can be extended to the idea of curriculum learning, where the agent incrementally accumulates knowledge over a sequence of tasks (i.e. a curriculum). In most existing work, such curricula have been constructed manually. Furthermore, they are fixed ahead of time, and do not adapt to the progress or abilities of the agent. In this paper, we formulate the design of a curriculum as a Markov Decision Process, which directly models the accumulation of knowledge as an agent interacts with tasks, and propose a method that approximates an execution of an optimal policy in this MDP to produce an agent-specific curriculum. We use our approach to automatically sequence tasks for 3 agents with varying sensing and action capabilities in an experimental domain, and show that our method produces curricula customized for each agent that improve performance relative to learning from scratch or using a different agent's curriculum.

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Kantert, Jan, Sarah Edenhofer, Sven Tomforde, Jörg Hähner, and Christian Müller-Schloer. "Defending Autonomous Agents Against Attacks in Multi-Agent Systems Using Norms." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005202101490156.

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Reports on the topic "Artificial autonomous agent"

Toner, Helen, John Bansemer, Kyle Crichton, et al. Through the Chat Window and Into the Real World: Preparing for AI Agents. Center for Security and Emerging Technology, 2024. http://dx.doi.org/10.51593/20240034.

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Computer scientists have long sought to build systems that can actively and autonomously carry out complicated goals in the real world—commonly referred to as artificial intelligence "agents." Recently, significant progress in large language models has fueled new optimism about the prospect of building sophisticated AI agents. This CSET-led workshop report synthesizes findings from a May 2024 workshop on this topic, including what constitutes an AI agent, how the technology is improving, what risks agents exacerbate, and intervention points that could help.

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Doo, Johnny. The Use of eVTOL Aircraft for First Responder, Police, and Medical Transport Applications. SAE International, 2023. http://dx.doi.org/10.4271/epr2023020.

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<div class="section abstract"><div class="htmlview paragraph">Advancements in electric vertical takeoff and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry. One particularly promising application involves on-demand, rapid-response use cases to broaden first responders, police, and medical transport mission capabilities. With the dynamic and varying public service operations, eVTOL aircraft can offer potentially cost-effective aerial mobility components to the overall solution, including significant lifesaving benefits.</div><div class="htmlview paragraph"><b>Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects</b> discusses the challenges need to be addressed before identified capabilities and benefits can be realized at scale: <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Mission-specific eVTOL vehicle development </div></li><li class="list-item"><div class="htmlview paragraph">Operator- and patient-specific accommodations</div></li><li class="list-item"><div class="htmlview paragraph">Detect-and-avoid capabilities in complex and challenging operating environments</div></li><li class="list-item"><div class="htmlview paragraph">Autonomous and artificial intelligence-enhanced mission capabilities</div></li><li class="list-item"><div class="htmlview paragraph">Home-base charging systems for battery power platforms</div></li><li class="list-item"><div class="htmlview paragraph">Simplified operator and support training</div></li><li class="list-item"><div class="htmlview paragraph"> Vehicle/fleet maintenance and support</div></li><li class="list-item"><div class="htmlview paragraph">Acceptance and participation from stakeholder services, local and state-level leadership, field operators, and support team members</div></li></ul></div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—such as ChatGPT or Gemini—capable of producing text, images, or decisions based on prompts. However, it is the third wave, embodied by AI agents, that poses the greatest challenge: software capable of autonomous action, making decisions on behalf of users, interacting across platforms, and executing tasks with minimal human oversight.

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