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Journal articles on the topic 'Agents and autonomous systems'

Author: Grafiati

Published: 25 May 2024

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1

Endriss, Ulle, Ann Nowé, Maria Gini, et al. "Autonomous agents and multiagent systems." AI Matters 7, no. 3 (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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2

Bhargava, Hemant K., and William C. Branley. "Simulating belief systems of autonomous agents." Decision Support Systems 14, no. 4 (1995): 329–48. http://dx.doi.org/10.1016/0167-9236(94)00036-r.

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3

Card, Alexander. "Structure Editors and Autonomous Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (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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Košeckà, Jana, and Ruzena Bajcsy. "Discrete Event Systems for autonomous mobile agents." Robotics and Autonomous Systems 12, no. 3-4 (1994): 187–98. http://dx.doi.org/10.1016/0921-8890(94)90025-6.

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5

JOHNSON, W. LEWIS, and BARBARA HAYES-ROTH. "The First Autonomous Agents Conference." Knowledge Engineering Review 13, no. 2 (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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Maes, Pattie. "Modeling Adaptive Autonomous Agents." Artificial Life 1, no. 1_2 (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 (1999): 1149. http://dx.doi.org/10.1109/tra.1999.817684.

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8

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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Langley, Pat, Ben Meadows, Mohan Sridharan, and Dongkyu Choi. "Explainable Agency for Intelligent Autonomous Systems." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (2017): 4762–63. http://dx.doi.org/10.1609/aaai.v31i2.19108.

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As intelligent agents become more autonomous, sophisti- cated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player’s path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more selfreliant, people will require that they explain their behaviors on demand. The more impressive these agents’ abilities, the more essential that we be able to understand them.

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Ur Rehman, Shafiq, and Aamer Nadeem. "An Approach to Model Based Testing of Multiagent Systems." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/925206.

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Autonomous agents perform on behalf of the user to achieve defined goals or objectives. They are situated in dynamic environment and are able to operate autonomously to achieve their goals. In a multiagent system, agents cooperate with each other to achieve a common goal. Testing of multiagent systems is a challenging task due to the autonomous and proactive behavior of agents. However, testing is required to build confidence into the working of a multiagent system. Prometheus methodology is a commonly used approach to design multiagents systems. Systematic and thorough testing of each interaction is necessary. This paper proposes a novel approach to testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. These interactions include percepts and actions along with messages between the agents which can be modeled in a protocol diagram. The protocol diagram is converted into a protocol graph, on which different coverage criteria are applied to generate test paths that cover interactions between the agents. A prototype tool has been developed to generate test paths from protocol graph according to the specified coverage criterion.

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11

Benyon, D., and D. Murray. "Adaptive systems: from intelligent tutoring to autonomous agents." Knowledge-Based Systems 6, no. 4 (1993): 197–219. http://dx.doi.org/10.1016/0950-7051(93)90012-i.

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12

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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13

Talukdar, Sarosh N. "Collaboration rules for autonomous software agents." Decision Support Systems 24, no. 3-4 (1999): 269–78. http://dx.doi.org/10.1016/s0167-9236(98)00070-0.

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Zhang, Kun, Yoichiro Maeda, and Yasutake Takahashi. "Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (2011): 896–903. http://dx.doi.org/10.20965/jaciii.2011.p0896.

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Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward redistribution among agents to reinforce group behavior, and we propose a method of constructing a multi-agent system with an autonomous group creation ability. This is able to strengthen the cooperative behavior of the group as social agents.

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

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16

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

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

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

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19

Gelenbe, Erol, Khaled Hussain, and Varol Kaptan. "Simulating autonomous agents in augmented reality." Journal of Systems and Software 74, no. 3 (2005): 255–68. http://dx.doi.org/10.1016/j.jss.2004.01.016.

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20

Dahlstedt, Palle, and Peter McBurney. "Musical Agents: Toward Computer-Aided Music Composition Using Autonomous Software Agents." Leonardo 39, no. 5 (2006): 469–70. http://dx.doi.org/10.1162/leon.2006.39.5.469.

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The authors, a composer and a computer scientist, discuss their collaborative research on the use of multiagent systems and their applicability to music and musical composition. They describe the development of software and techniques for the composition of generative music.

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Deng, Zhenhua, and Yiguang Hong. "Multi-Agent Optimization Design for Autonomous Lagrangian Systems." Unmanned Systems 04, no. 01 (2016): 5–13. http://dx.doi.org/10.1142/s230138501640001x.

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In this paper, distributed optimization control for a group of autonomous Lagrangian systems is studied to achieve an optimization task with local cost functions. To solve the problem, two continuous-time distributed optimization algorithms are designed for multiple heterogeneous Lagrangian agents with uncertain parameters. The proposed algorithms are proved to be effective for those heterogeneous nonlinear agents to achieve the optimization solution in the semi-global sense, even with the exponential convergence rate. Moreover, simulation adequately illustrates the effectiveness of our optimization algorithms.

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22

ADACHER, LUDOVICA, ALESSANDRO AGNETIS, and CARLO MELONI. "Autonomous agents architectures and algorithms in flexible manufacturing systems." IIE Transactions 32, no. 10 (2000): 941–51. http://dx.doi.org/10.1080/07408170008967452.

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23

Wong, Johnny S. K., and Armin R. Mikler. "Intelligent mobile agents in large distributed autonomous cooperative systems." Journal of Systems and Software 47, no. 2-3 (1999): 75–87. http://dx.doi.org/10.1016/s0164-1212(99)00027-8.

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24

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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25

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 (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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26

Prada, Rui. "Autonomous agents to accelerate extended reality testing." EU Research - The necessity of science in uncertain times Summer 2022, no. 31 (2022): 42–43. http://dx.doi.org/10.56181/anyp4112.

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Extended reality systems (XR) are used in a wide range of sectors, yet they need to be tested before they can reach the commercial market. We spoke to Rui Filipe Fernandes Prada about the work of the iv4XR project in developing autonomous testing agents that promise to bring significant benefits to developers.

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SUGIMURA, Nobuhiro, and Koji IWAMURA. "2201 DYNAMIC INTEGRATED PROCESS PLANNING AND PRODUCTION CONTROL IN FLEXIBLE MANUFACTURING SYSTEMS WITH AUTONOMOUS AGENTS." Proceedings of Manufacturing Systems Division Conference 2007 (2007): 37–38. http://dx.doi.org/10.1299/jsmemsd.2007.37.

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28

Botchkaryov, A. "METHOD FOR DECENTRALIZED CONTROL OF ADAPTIVE DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS." Computer systems and network 5, no. 1 (2023): 8–19. http://dx.doi.org/10.23939/csn2023.01.008.

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The problem of monitoring a computer network under conditions of limitations on the use of system resources and high requirements for the survivability of the monitoring system has been considered. An autonomous decentralized computer network monitoring system has been developed, consisting of a team of software agents. Each agent can operate in two modes: main mode and monitoring system management console mode. In the main mode, the agent collects information about the computer network. In management console mode, the agent provides the user with access to information collected by all agents and allows the user to execute commands to manage the monitoring system. The developed monitoring system allows you to obtain more reliable information about the operation of the network with greater efficiency under the conditions of limitations on the use of system resources specified by the user. The autonomous monitoring system is created on the basis of the concept of multi-agent systems, within which a software agent of the system has some initiative for planning and implementing monitoring scenarios. The operation of software agents implements methods for organizing adaptive processes for collecting information using the principles of self-organization and the concept of structural adaptation. A decentralized software architecture for an autonomous monitoring system without a control center has been proposed. This ensures high reliability and survivability of the monitoring system. The software architecture of the autonomous monitoring system implements the SMA application software interface and the corresponding software library, which allows you to collect statistical data on the operation of the computer network and its nodes. The implementation of a software agent and a management console for an autonomous computer network monitoring system has been considered. Key words: computer network monitoring, autonomous system, decentralized control, software agent

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Langley, Pat. "Agents of Exploration and Discovery." AI Magazine 42, no. 4 (2022): 72–82. http://dx.doi.org/10.1609/aimag.v42i4.15089.

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Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the problem, identify component functions, describe current abilities, and note remaining limitations. Finally, I discuss obstacles that the community must overcome before it can develop integrated agents of exploration and discovery.

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Langley, Pat. "Agents of Exploration and Discovery." AI Magazine 42, no. 4 (2022): 72–82. http://dx.doi.org/10.1609/aaai.12021.

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Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the problem, identify component functions, describe current abilities, and note remaining limitations. Finally, I discuss obstacles that the community must overcome before it can develop integrated agents of exploration and discovery.

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Ziemke, Tom. "Adaptive Behavior in Autonomous Agents." Presence: Teleoperators and Virtual Environments 7, no. 6 (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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Monteserin, Ariel, and Analía Amandi. "Argumentation–based negotiation planning for autonomous agents." Decision Support Systems 51, no. 3 (2011): 532–48. http://dx.doi.org/10.1016/j.dss.2011.02.016.

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33

Espinosa-Leal, Leonardo, Anthony Chapman, and Magnus Westerlund. "Autonomous Industrial Management via Reinforcement Learning." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8427–39. http://dx.doi.org/10.3233/jifs-189161.

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Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper, we indicate the distinctions between automated and autonomous systems as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, how to train reinforcement learning agents to learn specific tasks through generalisation. Once generalisation is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment that focuses on how the agents can adapt to different environments. We introduce an initial prototype of our ideas by solving a multi-armed bandit problem using two ε-greedy algorithms. Further, we discuss future applications in the industrial management realm and propose a modular architecture for improving the decision-making process via autonomous agents.

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PELACHAUD, CATHERINE, and ISABELLA POGGI. "Multimodal embodied agents." Knowledge Engineering Review 17, no. 2 (2002): 181–96. http://dx.doi.org/10.1017/s0269888902000218.

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1 Believable interactive embodied agentsAmong the goals of research on autonomous agents one important aim is to build believable interactive embodied agents that are apt to application to friendly interfaces in e-commerce, tourist and service query systems, entertainment (e.g. synthetic actors) and education (pedagogical agents, agents for help and instruction to the hearing impaired).

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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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Casadei, Roberto, Gianluca Aguzzi, and Mirko Viroli. "A Programming Approach to Collective Autonomy." Journal of Sensor and Actuator Networks 10, no. 2 (2021): 27. http://dx.doi.org/10.3390/jsan10020027.

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Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

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Fuchs, Andrew, Andrea Passarella, and Marco Conti. "Compensating for Sensing Failures via Delegation in Human–AI Hybrid Systems." Sensors 23, no. 7 (2023): 3409. http://dx.doi.org/10.3390/s23073409.

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Given the increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g., perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human–AI teaming case where a managing agent is tasked with identifying when to perform a delegated assignment and whether the human or autonomous system should gain control. In this context, the manager will estimate its best action based on the likelihood of either (human, autonomous) agent’s failure as a result of their sensing capabilities and possible deficiencies. We model how the environmental context can contribute to, or exacerbate, these sensing deficiencies. These contexts provide cases where the manager must learn to identify agents with capabilities that are suitable for decision-making. As such, we demonstrate how a reinforcement learning manager can correct the context–delegation association and assist the hybrid team of agents in outperforming the behavior of any agent working in isolation.

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Langley, Pat. "Open-World Learning for Radically Autonomous Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13539–43. http://dx.doi.org/10.1609/aaai.v34i09.7078.

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In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approaches to the empirical study of such open-world learning.

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Wai, Sim Keng, Cheah WaiShiang, Muhammad Asyraf Bin Khairuddin, Yanti Rosmunie Binti Bujang, Rahmat Hidayat, and Celine Haren Paschal. "Autonomous Agents in 3D Crowd Simulation Through BDI Architecture." JOIV : International Journal on Informatics Visualization 5, no. 1 (2021): 1. http://dx.doi.org/10.30630/joiv.5.1.371.

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Agent based simulation (ABS) is a paradigm to modelling systems included of autonomous and interacting agents. ABS has been tremendous growth and used by researchers in the social sciences to study socio-environmental complex systems. To date, various platforms have been introduced for agent-based social simulation. They are rule based in any logic, python based in SPADE and etc. Although those platforms have been introduced, there is still an insufficient to develop a crowd simulation in 3D platform. Having a 3D platform is needed to enabling the crowd simulation for training purposes. However, the current tools and platform still lack features to develop and simulate autonomous agents in the 3D world. This paper introduced a BDI plug in at Unity3D for crowd simulation. BDI is an intelligent agent architecture and it is able to develop autonomous agents in crowd environment. In this paper, we present the BDI plug with a case study of Australia bush fire and discuss a method to support autonomous agents' development in 3D crowd simulation. The tool allows the modeller to develop autonomous agents in 3D world by taking the advantages of Unity3D.

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40

Kaelbling, Leslie Pack. "Foundations of learning in autonomous agents." Robotics and Autonomous Systems 8, no. 1-2 (1991): 131–44. http://dx.doi.org/10.1016/0921-8890(91)90018-g.

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Seeber, Isabella, Lena Waizenegger, Stefan Seidel, Stefan Morana, Izak Benbasat, and Paul Benjamin Lowry. "Collaborating with technology-based autonomous agents." Internet Research 30, no. 1 (2020): 1–18. http://dx.doi.org/10.1108/intr-12-2019-0503.

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PurposeThis article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work.Design/methodology/approachThe panelists (Drs Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision-making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis for a moderated panel discussion (the moderators were Drs Isabella Seeber and Lena Waizenegger), during which the initial position statements were elaborated on and additional issues were raised.FindingsThrough the discussion, a set of additional issues were identified. These issues related to (1) the design of autonomous technology-based agents in terms of human–machine workplace configurations, as well as transparency and explainability, and (2) the unintended consequences of using autonomous technology-based agents in terms of de-evolution of social interaction, prioritization of machine teammates, psychological health, and biased algorithms.Originality/valueKey issues related to the affective and cognitive implications of using autonomous technology-based agents, design issues, and unintended consequences highlight key contemporary research challenges that allow researchers in this area to leverage compelling questions that can guide further research in this field.

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Yamamoto, Masahito, Takashi Kawakami, and Keitaro Naruse. "Special Issue on Machine Learning for Robotics and Swarm Systems." Journal of Robotics and Mechatronics 31, no. 4 (2019): 519. http://dx.doi.org/10.20965/jrm.2019.p0519.

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In recent years, machine-learning applications have been rapidly expanding in the fields of robotics and swarm systems, including multi-agent systems. Swarm systems were developed in the field of robotics as a kind of distributed autonomous robotic systems, imbibing the concepts of the emergent methodology for extremely redundant systems. They typically consist of homogeneous autonomous robots, which resemble living animals that build swarms. Machine-learning techniques such as deep learning have played a remarkable role in controlling robotic behaviors in the real world or multi-agents in the simulation environment. In this special issue, we highlight five interesting papers that cover topics ranging from the analysis of the relationship between the congestion among autonomous robots and the task performances, to the decision making process among multiple autonomous agents. We thank the authors and reviewers of the papers and hope that this special issue encourages readers to explore recent topics and future studies in machine-learning applications for robotics and swarm systems.

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Jiao, Wenpin, Yanchun Sun, and Hong Mei. "Automated assembly of Internet-scale software systems involving autonomous agents." Journal of Systems and Software 83, no. 10 (2010): 1838–50. http://dx.doi.org/10.1016/j.jss.2010.04.074.

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Núñez, Manuel, Ismael Rodríguez, and Fernando Rubio. "Specification and testing of autonomous agents in e-commerce systems." Software Testing, Verification and Reliability 15, no. 4 (2005): 211–33. http://dx.doi.org/10.1002/stvr.323.

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Khoshnoud, Farbod, Ibrahim I. Esat, Clarence W. de Silva, and Marco B. Quadrelli. "Quantum Network of Cooperative Unmanned Autonomous Systems." Unmanned Systems 07, no. 02 (2019): 137–45. http://dx.doi.org/10.1142/s2301385019500055.

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A quantum network may be realized by the entanglement of particles communicated by qubits between quantum computers, where the entangled photons of light are transferred for communication purposes. This technology has been proven to be feasible experimentally through free-space distribution of entangled photon pairs. Sending photons of light through nonlinear crystals produces correlated photon pairs, by splitting each photon into two half particles with each particle having the same level of energy, which results in entangled pairs. This entanglement is represented by photons, having both either horizontal or vertical polarization. This paper investigates collaborative robotic tasks of unmanned systems in a network where the agents are entangled. For instance, a leader robot sends two identical photons (e.g. with vertical polarization) to two follower robots/autonomous vehicles to communicate information about various tasks such as swarm, formation, trajectory tracking, path following and collaborative tasks. The potential advantages of quantum cooperation of robotic agents is the speed of the process, the ability to achieve security with immunity against cyberattacks, and fault tolerance, through entanglement. If a Quantum Network is implemented in a robotic application, it would present an effective solution; for example, for a group of unmanned systems working securely together. An analytical basis of such systems is investigated in this paper, and the formulation of quantum cooperation of unmanned systems is presented and discussed. The concept of experimental quantum entanglement, as well as quantum cryptography (QC), for robotics applications is presented.

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Raji, F., and B. Tork Ladani. "Anonymity and security for autonomous mobile agents." IET Information Security 4, no. 4 (2010): 397. http://dx.doi.org/10.1049/iet-ifs.2009.0217.

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Akarsh, Shagun, Avadh Kishor, Rajdeep Niyogi, Alfredo Milani, and Paolo Mengoni. "Social Cooperation in Autonomous Agents to Avoid the Tragedy of the Commons." International Journal of Agricultural and Environmental Information Systems 8, no. 2 (2017): 1–19. http://dx.doi.org/10.4018/ijaeis.2017040101.

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In this paper, we address the “Tragedy of the Commons” (TOC) problem for shared-resource systems by considering different types of behaviors of agents. On one extreme are self-interested agents while on the other one, agents are concerned about the welfare of the society. Algorithms to capture the different behaviors of the agents with and without interaction among the agents are proposed. An extensive experimental analysis for the different cases has been carried out as well as comparisons of our algorithms with an existing approach. Our study shows that if the agents are willing to sacrifice for some period of time, the sustainability of the society increases considerably.

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BRENNAN, ROBERT W. "Holonic and multi-agent systems in industry." Knowledge Engineering Review 16, no. 4 (2001): 375–81. http://dx.doi.org/10.1017/s0269888901000200.

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The concept of holonic systems has its roots in the desire to understand the structure of natural systems (e.g. living organisms and social organisations) and in particular their ability to behave in a stable yet flexible manner in the face of change. It is not surprising that the lessons learned from these natural systems could help with the design and control of complex man-made systems. However, a key issue is, how can one translate holonic concepts to real industrial environments? For example, one of the key holonic concepts, the holon, can be described as a self-contained autonomous and cooperative entity; when deciding how to implement holons, software agents appear to be the logical choice. In this paper, we summarise the presentations and discussions from a workshop held at the recent International Conference on Autonomous Agents that focused on this issue and brought together researchers from both the holonic systems and the multi-agents systems communities.

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Tabrez, Aaquib. "Autonomous Policy Explanations for Effective Human-Machine Teaming." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23423–24. http://dx.doi.org/10.1609/aaai.v38i21.30412.

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Policy explanation, a process for describing the behavior of an autonomous system, plays a crucial role in effectively conveying an agent's decision-making rationale to human collaborators and is essential for safe real-world deployments. It becomes even more critical in effective human-robot teaming, where good communication allows teams to adapt and improvise successfully during uncertain situations by enabling value alignment within the teams. This thesis proposal focuses on improving human-machine teaming by developing novel human-centered explainable AI (xAI) techniques that empower autonomous agents to communicate their capabilities and limitations via multiple modalities, teach and influence human teammates' behavior as decision-support systems, and effectively build and manage trust in HRI systems.

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Keijzer, Fred A., and Sacha Bem. "Behavioral systems interpreted as autonomous agents and as coupled dynamical systems: A criticism." Philosophical Psychology 9, no. 3 (1996): 323–46. http://dx.doi.org/10.1080/09515089608573187.

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