Thematische Bibliographien / Agents and autonomous systems

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Auswahl der wissenschaftlichen Literatur zum Thema „Agents and autonomous systems“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Agents and autonomous systems"

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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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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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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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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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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Dissertationen zum Thema "Agents and autonomous systems"

Meshref, Hossam. "Modeling Autonomous Agents' Behavior Using Neuro-Immune Networks." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/28473.

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Autonomous robots are expected to interact with their dynamic changing environment. This interactions requires certain level of behavior based Intelligence, which facilitates the dynamic adaptation of the robot behavior accordingly with his surrounding environment. Many researches have been done in biological information processing systems to model the behavior of an autonomous robot. The Artificial Immune System (AIS) provides new paradigm suitable for dynamic problem dealing with unknown environment rather than a static problem. The immune system has some features such as memory, tolerance, diversity and more features that can be used in engineering applications. The immune system has an important feature called meta-dynamics in which new species of antibodies are produced continuously from the bone marrow. If the B-Cell (robot) cannot deal with the current situation, new behaviors (antibodies) should be generated by the meta dynamics function. This behavior should be incorporated into the existing immune system to gain immunity against new environmental changes. We decided to use a feed forward Artificial Neural Network (ANN) to simulate this problem, and to build the AIS memory. Many researchers have tried to tackle different points in mimicking the biological immune system, but no one previously has proposed such an acquired memory. This contribution is made as a "proof of concept" to the field of biological immune system simulation as a start of further research efforts in this direction. Many applications can potentially use our designed Neuro-Immune Network (NIN), especially in the area of autonomous robotics. We demonstrated the use of the designed NIN to control a robot arm in an unknown environment. As the system encounters new cases, it will increase its ability to deal with old and new situations encountered. This novel technique can be applied to many robotics applications in industry, where autonomous robots are required to have adaptive behavior in response to their environmental changes. Regarding future work, the use of VLSI neural networks to enhance the speed of the system for real time applications can be investigated along with possible methods of design and implementation of a similar VLSI chip for the AIN.<br>Ph. D.

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Jarne, Ornia Daniel. "Ant Colony Algorithms andits applications to Autonomous Agents Systems." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217391.

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With the latest advancements in autonomous agents systems and technology, there is a growing interest in developing control algorithms and methods to coordinate large numbers of robotic entities. Following this line of work, the use of biologically inspired algorithms based on swarm emerging behaviour presents some really interesting properties for controlling multiple agents. They rely on very simple instructions and communications to develop a coordinated structure in the system. Particularly, this master thesis focuses on the study of Ant Colony algorithms based on stigmergy interaction to coordinate agents and perform a certain task. The first part focuses on the theoretical background and algorithm convergence proof, while the second part consists of experimental simulations and results. For this, some metric parameters have been developed and found to be especially useful in the study of a simple path planning test case. The main concept developed in this work is an adaptation of Shannon Entropy that measures uniformity and order in the system and the weighted graph. This parameter has been used to study the performance and results of an autonomous agent system based on Ant Colony algorithms. Finally, this control algorithm has been modified to develop an event-triggered control scheme. Using the properties of the weighted graph (Entropy) and the sensing of the agents, a decentralized event-triggered method has been implemented and tested, and has been found to increase efficiency in the usage of system resources.<br>Med den senaste tidens utveckling inom autonoma agentsystem och teknologier, finns ett ökat intresse för utveckling av styralgoritmer och metoder för att koordinera stora mängder robotenheter. Inom detta område visar användandet av biologiskt inspirerade algoritmer, baserade på naturliga svärmbeteenden, intressanta egenskaper som kan utnyttjas i styrandet av system som innefattar ett flertal agenter. Dessa är uppbyggda av simpla instruktioner och kommunikationsmedel för att tillgodose struktur i systemet. I synnerhet fokuserar detta masterexamensarbete på studier av Ant Colony-algoritmer, baserade på stigmergy-interaktion för att koordinera enheter och få dem att utföra specifika uppgifter. Den första delen behandlar den teoretiska bakgrunden och konvergensbevis medan den andra delen i huvudsak består av experimentella simuleringar samt resultat. Till detta ändamål har metriska parametrar utvecklats, vilka ansågs särskilt användbara när planeringen av en enkel bana studerades. Huvudkonceptet som utvecklats i detta arbete är en tillämpning av Shannon- Entropi, vilket mäter enhetlighet och ordning i ett system samt den viktade grafen. Denna parameter har använts för att studera prestandan och resultaten hos ett autonomt agentsystem baserat på Ant Colony-algoritmer. Slutligen har denna styralgoritm modifierats för att utveckla ett händelsestyrt styrschema. Genom att använda egenskaperna hos den viktade grafen (entropi) tillsammans med sensorsystemet hos agentenheterna, så har en decentraliserad händelsestyrd metod implementerats, testats och visat sig ge ökad effektivitet gällande utnyttjandet av systemresurser.

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AL-Buraiki, Omar S. M. "Specialized Agents Task Allocation in Autonomous Multi-Robot Systems." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41504.

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With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders. Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversified in terms of applications. But in order to achieve these goals, the individual agents forming cooperative robotic systems need to be specialized in what they can accomplish, while ensuring accuracy and preserving the ability to perform diverse tasks. This thesis addresses the problem of task allocation in swarm robotics in the specific context where specialized capabilities of the individual agents are considered. Based on the assumption that each individual agent possesses specialized functional capabilities and that the expected tasks, which are distributed in the surrounding environment, impose specific requirements, the proposed task allocation mechanisms are formulated in two different spaces. First, a rudimentary form of the team members’ specialization is formulated as a cooperative control problem embedded in the agents’ dynamics control space. Second, an advanced formulation of agents’ specialization is defined to estimate the individual agents’ task allocation probabilities in a dedicated specialization space, which represents the core contribution of this thesis to the advancement and practice in the area of swarm robotics. The original task allocation process formulated in the specialization space evolves through four stages of development. First, a task features recognition stage is conceptually introduced to leverage the output of a sensing layer embedded in robotic agents to drive the proposed task allocation scheme. Second, a matching scheme is developed to best match each agent’s specialized capabilities with the corresponding detected tasks. At this stage, a general binary definition of agents’ specialization serves as the basis for task-agent association. Third, the task-agent matching scheme is expanded to an innovative probabilistic specialty-based task-agent allocation framework to generalize the concept and exploit the potential of agents’ specialization consideration. Fourth, the general framework is further refined with a modulated definition of the agents’ specialization based on their mechanical, physical structure, and embedded resources. The original framework is extended and a prioritization layer is also introduced to improve the system’s response to complex tasks that are characterized based on the recognition of multiple classes. Experimental validation of the proposed specialty-based task allocation approach is conducted in simulation and on real-world experiments, and the results are presented and discussed in light of potential applications to demonstrate the effectiveness and efficiency of the proposed framework.

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Hess, Traci J. "A Study of Autonomous Agents in Decision Support Systems." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/11277.

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Software agents have been heralded as the most important emerging technology of the decade. As software development firms eagerly attempt to integrate these autonomous programs into their products, researchers attempt to define the concept of agency and to develop architectures that will improve agent capabilities. Decision Support System (DSS) researchers have been eager to integrate agents into their applications, and exploratory works in which agents have been used within a DSS have been documented. This dissertation attempts to further this exploration by studying the agent features and underlying architectures that can lead to the successful integration of agents in DSS. This exploration is carried out in three parts. In the first part, a review of the relevant research streams is provided. The history and current status of software agents is first discussed. Similarly, a historical and current view of DSS research is provided. Lastly, a historical and tutorial-type of discussion is provided on the topic of Artificial Intelligence (AI) planning. This review of the relevant literature provides a general background for the conceptual analyses and implementations that are carried out in the next two sections. In the second part, the literature on software agents is synthesized to develop a definition of agency applicable to DSS. Using this definition, an agent-integrated DSS that supports variance-analysis is designed and developed. Following this implementation, a general framework for agent-enabling DSS is suggested. The use of this framework promises to raise some DSS to a new level of capability whereby "what-if" systems are transformed into real-time, proactive systems. The third part utilizes this general framework to agent-enable a corporate-planning system DSS and extends the framework in the second section through the introduction of an automated-planning agent. The agent uses AI planning to generate decision-making alternatives, providing a means to integrate and sequence the models in the DSS. The architecture used to support this planning agent is described. This new kind of DSS enables not only the monitoring of goals, but also the maintenance of these goals through agent-generated plans. The conclusion summarizes the contributions of this work and outlines in considerable detail potential research opportunities in the realm of software agents, DSS, and planning.<br>Ph. D.

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Faratin, Peyman. "Automated service negotiation between autonomous computational agents." Thesis, Queen Mary, University of London, 2000. http://qmro.qmul.ac.uk/xmlui/handle/123456789/28945.

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Multi-agent systems are a new computational approach for solving real world, dynamic and open system problems. Problems are conceptualized as a collection of decentralised autonomous agents that collaborate to reach the overall solution. Because of the agents autonomy, their limited rationality, and the distributed nature of most real world problems, the key issue in multi-agent system research is how to model interactions between agents. Negotiation models have emerged as suitable candidates to solve this interaction problem due to their decentralised nature, emphasis on mutual selection of an action, and the prevalence of negotiation in real social systems. The central problem addressed in this thesis is the design and engineering of a negotiation model for autonomous agents for sharing tasks and/or resources. To solve this problem a negotiation protocol and a set of deliberation mechanisms are presented which together coordinate the actions of a multiple agent system. In more detail, the negotiation protocol constrains the action selection problem solving of the agents through the use of normative rules of interaction. These rules temporally order, according to the agents' roles, communication utterances by specifying both who can say what, as well as when. Specifically, the presented protocol is a repeated, sequential model where offers are iteratively exchanged. Under this protocol, agents are assumed to be fully committed to their utterances and utterances are private between the two agents. The protocol is distributed, symmetric, supports bi and/or multi-agent negotiation as well as distributive and integrative negotiation. In addition to coordinating the agent interactions through normative rules, a set of mechanisms are presented that coordinate the deliberation process of the agents during the ongoing negotiation. Whereas the protocol normatively describes the orderings of actions, the mechanisms describe the possible set of agent strategies in using the protocol. These strategies are captured by a negotiation architecture that is composed of responsive and deliberative decision mechanisms. Decision making with the former mechanism is based on a linear combination of simple functions called tactics, which manipulate the utility of deals. The latter mechanisms are subdivided into trade-off and issue manipulation mechanisms. The trade-off mechanism generates offers that manipulate the value, rather than the overall utility, of the offer. The issue manipulation mechanism aims to increase the likelihood of an agreement by adding and removing issues into the negotiation set. When taken together, these mechanisms represent a continuum of possible decision making capabilities: ranging from behaviours that exhibit greater awareness of environmental resources and less to solution quality, to behaviours that attempt to acquire a given solution quality independently of the resource consumption. The protocol and mechanisms are empirically evaluated and have been applied to real world task distribution problems in the domains of business process management and telecommunication management. The main contribution and novelty of this research are: i) a domain independent computational model of negotiation that agents can use to support a wide variety of decision making strategies, ii) an empirical evaluation of the negotiation model for a given agent architecture in a number of different negotiation environments, and iii) the application of the developed model to a number of target domains. An increased strategy set is needed because the developed protocol is less restrictive and less constrained than the traditional ones, thus supporting development of strategic interaction models that belong more to open systems. Furthermore, because of the combination of the large number of environmental possibilities and the size of the set of possible strategies, the model has been empirically investigated to evaluate the success of strategies in different environments. These experiments have facilitated the development of general guidelines that can be used by designers interested in developing strategic negotiating agents. The developed model is grounded from the requirement considerations from both the business process management and telecommunication application domains. It has also been successfully applied to five other real world scenarios.

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Giansiracusa, Michelangelo Antonio. "A secure infrastructural strategy for safe autonomous mobile agents." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16052/1/Michaelangelo_Giansiracusa_Thesis.pdf.

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Portable languages and distributed paradigms have driven a wave of new applications and processing models. One of the most promising, certainly from its early marketing, but disappointing (from its limited uptake)is the mobile agent execution and data processing model. Mobile agents are autonomous programs which can move around a heterogeneous network such as the Internet, crossing through a number of different security domains, and perform some work at each visited destination as partial completion of a mission for their agent user. Despite their promise as a technology and paradigm to drive global electronic services (i.e.any Internet-driven-and-delivered service, not solely e-commerce related activities), their up take on the Internet has been very limited. Chief among the reasons for the paradigm's practical under-achievement is there is no ubiquitous frame work for using Internet mobile agents, and non-trivial security concerns abound for the two major stake holders (mobile agent users and mobile agent platform owners). While both stake holders have security concerns with the dangers of the mobile agent processing model, most investigators in the field are of the opinion that protecting mobile agents from malicious agent platforms is more problematic than protecting agent platforms from malicious mobile agents. Traditional cryptographic mechanisms are not well-suited to counter the bulk of the threats associated with the mobile agent paradigm due to the untrusted hosting of an agent and its intended autonomous, flexible movement and processing. In our investigation, we identified that the large majority of the research undertaken on mobile agent security to date has taken a micro-level perspective. By this we mean research focused solely on either of the two major stakeholders, and even then often only on improving measures to address one security issue dear to the stake holder - for example mobile agent privacy (for agent users) or access control to platform resources (for mobile agent platform owners). We decided to take a more encompassing, higher-level approach in tackling mobile agent security issues. In this endeavour, we developed the beginnings of an infrastructural-approach to not only reduce the security concerns of both major stakeholders, but bring them transparently to a working relationship. Strategic utilisation of both existing distributed system trusted-third parties (TTPs) and novel mobile agent paradigm-specific TTPs are fundamental in the infrastructural framework we have devised. Besides designing an application and language independent frame work for supporting a large-scale Internet mobile agent network, our Mobile Agent Secure Hub Infrastructure (MASHIn) proposal encompasses support for flexible access control to agent platform resources. A reliable means to track the location and processing times of autonomous Internet mobile agents is discussed, withfault-tolerant handling support to work around unexpected processing delays. Secure,highly-effective (incomparison to existing mechanisms) strategies for providing mobile agent privacy, execution integrity, and stake holder confidence scores were devised - all which fit comfortably within the MASHIn framework. We have deliberately considered the interests - withoutbias -of both stake holders when designing our solutions. In relation to mobile agent execution integrity, we devised a new criteria for assessing the robustness of existing execution integrity schemes. Whilst none of the existing schemes analysed met a large number of our desired properties for a robust scheme, we identified that the objectives of Hohl's reference states scheme were most admirable - particularly real - time in - mission execution integrity checking. Subsequently, we revised Hohl's reference states protocols to fit in the MASHIn framework, and were able to overcome not only the two major limitations identified in his scheme, but also meet all of our desired properties for a robust execution integrity scheme (given an acceptable decrease in processing effiency). The MASHIn offers a promising new perspective for future mobile agent security research and indeed a new frame work for enabling safe and autonomous Internet mobile agents. Just as an economy cannot thrive without diligent care given to micro and macro-level issues, we do not see the security prospects of mobile agents (and ultimately the prospects of the mobile agent paradigm) advancing without diligent research on both levels.

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Giansiracusa, Michelangelo Antonio. "A Secure Infrastructural Strategy for Safe Autonomous Mobile Agents." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16052/.

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Abu, Bakar Badril. "Autonomous multi-agent reconfigurable control systems." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/351346/.

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This thesis is an investigation of methods and architectures for autonomous multi-agent reconfigurable controllers. As part of the analysis two components are looked at: the fault detection and diagnosis (FDD) component and the controller reconfiguration (CR) component. The FDD component detects and diagnoses faults. The CR component on the other hand, adapts or changes the control architecture to accommodate the fault. The problem is to synchronize or integrate these two components in the overall structure of a control system. A novel approach is proposed. A multiagent architecture is used to interface between the two components. This method allows the system to be viewed as a modular structure. Three types of agent are defined. A planner agent Ap, a monitor agent Am and a control agent Ac. The monitor agent takes the role of the FDD component. The planner and control agents on the other hand take the roles of CR component. The planner decides which controller to use and passes it on to Ac. It also decides on the parameter settings of the system and changes it accordingly. It belongs to the reactive agent category. The planner agent's internal architecture maps its sensor data directly to actions using a pre-set rule based conditional logic. It was decided that this architecture would reduce the overall complexity of the system. The monitor agent Am belongs to the learning agent category. It uses an algorithm called adaptive resonance theory neural network or ART-NN to autonomously categorize system faults. Am then informs the other agents of the fault status. ART-NN was chosen due to the fact that it does not need to be trained with sample data and learns to categorize data patterns on the fly. This allows Am to detect unmodelled system faults. The control agent Ac also belongs to the learning agent category. It uses a multiagent reinforcement learning algorithm to learn a controller for the system at hand. Once a suitable controller has been learnt, the parameters of the controller are passed to Ap for it to be stored in its memory and learning is terminated. During control execution mode, controller parameters are sent to Ac from Ap. The novel approach is demonstrated on a case study. Our laboratory-built 4-wheeled skid-steering vehicle complete with sensors is designed as a way of demonstration. Several faults are simulated and the response of the demo system is analyzed.

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LIMA, Allan Diego Silva. "FAct: Um framework para a construção de sistemas multiatores." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/1804.

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Made available in DSpace on 2014-06-12T15:52:31Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009<br>Atores Sintéticos são agentes inteligentes que possuem personalidade, atuam em ambientes multimídias ou virtuais e são representados graficamente por avatares. Estas entidades são utilizadas em sistemas de simuladores como forma de enriquecer a interação entre a aplicação e seus usuários, proporcionando assim aumento da credibilidade do projeto. Com a sua popularização sugiram vários projetos que os utilizam. Estes projetos têm diversos aspectos em comum. Por exemplo, neles seus atores se comunicam e possuem modelos de personalidade. Contudo, apesar das similaridades os projetos encontrados na literatura foram implementados de forma independente. Ou seja, tiveram seu código fonte desenvolvido do zero. Porém, no âmbito dos sistemas multiagentes tal problema não ocorre devido à existência de diversos frameworks multiagentes que agrupam as funcionalidades comuns a tais sistemas, fazendo com que o desenvolvedor implemente apenas o que é relativo ao seu contexto de simulação. Porém, estes frameworks não são utilizados em sistemas multiarores, pois não suportam as peculiaridades dos sistemas multiatores. Buscando solucionar esse problema, este trabalho apresenta o FAct (Framework for Actors), um projeto criado especificamente para auxiliar na construção sistemas multiatores. Ele focado nas características peculiares destes sistemas e tem como principal objetivo reduzir o custo e tempo de desenvolvimento para os projetos baseados nesta tecnologia. Como forma de exemplificar o uso do FAct e avaliar o seu impacto no desenvolvimento de simuladores multiatores, serão apresentados dois simuladores implementados com base no framework

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Lutzhöft, Margareta. "Self-organised communication in autonomous agents: A critical evaluation of artificial life models." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-485.

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<p>This dissertation aims to provide a critical evaluation of artificial life (A-Life) models of communication in autonomous agents. In particular the focus will be on the issue of self-organisation, which is often argued to be one of the characteristic features distinguishing A-life from other approaches. To ground the arguments, a background of the study of communication within artificial intelligence is provided. This is followed by a comprehensive review of A-Life research on communication between autonomous agents, which is evaluated by breaking down self-organisation into the following sub-questions. Is communication self-organised or hard-coded? What do signals mean to the agents, and how should an external examiner interpret them? Is there any spatial or temporal displacement, or do agents only communicate about their present situation? It is shown that there is very little self-organised communication, as yet, when examined on these grounds, and that most models only look at communication as relatively independent from other behaviours. As a conclusion, it is suggested to use integrated co-evolution of behaviours, including communication, in the spirit of the enactive cognitive science paradigm, and by using incremental evolution combined with learning.</p>

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Bücher zum Thema "Agents and autonomous systems"

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

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

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

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

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Calvaresi, Davide, Amro Najjar, Michael Winikoff, and Kary Främling, eds. Explainable, Transparent Autonomous Agents and Multi-Agent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51924-7.

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Calvaresi, Davide, Amro Najjar, Michael Schumacher, and Kary Främling, eds. Explainable, Transparent Autonomous Agents and Multi-Agent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30391-4.

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Gorodetsky, Vladimir, Jiming Liu, and Victor A. Skormin, eds. Autonomous Intelligent Systems: Agents and Data Mining. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b136855.

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Jain, L. C., and Dilip Kumar Pratihar. Intelligent autonomous systems: Foundations and applications. Springer Verlag, 2010.

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Coordinating plans of autonomous agents. Springer-Verlag, 1992.

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Pěchouček, Michal, Simon G. Thompson, and Holger Voos, eds. Defence Industry Applications of Autonomous Agents and Multi-Agent Systems. Birkhäuser Basel, 2008. http://dx.doi.org/10.1007/978-3-7643-8571-2.

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Buchteile zum Thema "Agents and autonomous systems"

Kampik, Timotheus, Juan Carlos Nieves, and Helena Lindgren. "Empathic Autonomous Agents." In Engineering Multi-Agent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25693-7_10.

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Fukuda, Toshio, and Kosuke Sekiyama. "Cooperative behavior between autonomous agents." In Autonomous Robotic Systems. Springer London, 1998. http://dx.doi.org/10.1007/bfb0030802.

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Purvis, Maryam, Martin Purvis, Azhar Haidar, and Bastin Tony Roy Savarimuthu. "A Distributed Workflow System with Autonomous Components." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32128-6_15.

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Xu, Junhong, Durgakant Pushp, Kai Yin, and Lantao Liu. "Decision-Making Among Bounded Rational Agents." In Distributed Autonomous Robotic Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51497-5_20.

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Boella, Guido, Leendert van der Torre, and Serena Villata. "Changing Institutional Goals and Beliefs of Autonomous Agents." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_11.

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Shim, Yunju, and Minkoo Kim. "Automatic Short Story Generator Based on Autonomous Agents." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45680-5_11.

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Tzafestas, E. S. "Autonomous Agents in Cellular Manufacturing." In Advances in Intelligent Autonomous Systems. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4790-3_19.

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Márton, Lőrinc, Piroska Haller, Tamás Szabó, Hunor Sándor, Tamás Vajda, and Zoltán Szántó. "Network Controller for Teleoperated Mobile Robotic Agents." In Intelligent Autonomous Systems 13. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_103.

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Collenette, Joe, Katie Atkinson, Daan Bloembergen, and Karl Tuyls. "Modelling Mood in Co-operative Emotional Agents." In Distributed Autonomous Robotic Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73008-0_39.

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Red’ko, Vladimir G. "Models of Autonomous Cognitive Agents." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32554-5_2.

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Konferenzberichte zum Thema "Agents and autonomous systems"

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

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

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

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

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

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Zhang, Guoxian, Devendra P. Garg, and Greg Fricke. "Hazardous Spill Perimeter Detection and Monitoring via Multiple Autonomous Mobile Robotic Agents." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4160.

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The problem of perimeter detection and monitoring has a variety of applications. In this paper, a hybrid system with finite states strategy is proposed for multiple autonomous robotic agents with the purpose of hazardous spill perimeter detection and tracking. In the system, each robotic agent is assumed to be in one of the following states: searching, pursuing, and tracking. The agents are prioritized based on their states, and a potential field is constructed for agents in each state. The location and velocity of an agent in the tracking state as well as those of its closest leading and trailing agents are utilized to control its movement. The convergence of the tracking algorithm is analyzed for multiple spills under certain conditions. Simulation results show that with the proposed method, the agents can successfully detect and track the spills of various sizes and movements.

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Hagras, H., and M. Colley. "Collaborating multi robotic agents for operations in inaccessible environments." In IEE Forum on: Autonomous Systems. IEE, 2005. http://dx.doi.org/10.1049/ic:20050470.

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Mostafa, Salama A., Mohd Sharifuddin Ahmad, Azhana Ahmad, Muthukkaruppan Annamalai, and Saraswathy Shamini Gunasekaran. "An autonomy viability assessment matrix for agent-based autonomous systems." In 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR). IEEE, 2015. http://dx.doi.org/10.1109/isamsr.2015.7379130.

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Wu, H. "Multi-agent based automation platform for distributed industrial systems." In IEE Seminar on Autonomous Agents in Control. IEE, 2005. http://dx.doi.org/10.1049/ic:20050179.

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Khan, Shahed, Sifat Momen, Nabeel Mohammed, and Nafees Mansoor. "Patterns of Flocking in Autonomous Agents." In 2018 International Conference on Intelligent Autonomous Systems (ICoIAS). IEEE, 2018. http://dx.doi.org/10.1109/icoias.2018.8494115.

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Holvoet, T. "Decentralized control of autonomous guided vehicles for scalable warehouse systems." In IEE Seminar on Autonomous Agents in Control. IEE, 2005. http://dx.doi.org/10.1049/ic:20050178.

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Kamath Barkur, Sudarshan, Pratik Sitapara, Sven Leuschner, and Sigurd Schacht. "Magenta: Metrics and Evaluation Framework for Generative Agents based on LLMs." In Intelligent Human Systems Integration (IHSI 2024) Integrating People and Intelligent Systems. AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004478.

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Large Language Models (LLMs) have emerged as a driving force in the field of Natural Language Processing (NLP) with applications spanning various domains, including the development of Autonomous Generative Agents. Generative Agents are computational software programs designed to believably simulate human behavior by harnessing the capabilities of large language models. Through repetitive prompts against the large language model, these agents operate based on a system architecture consisting of memory streams, reflection, and planning, allowing them to store experiences, learn from them, and translate insights into high-level action plans to interact with their environment. This paper discusses the current landscape of language models and autonomous agents, their advantages and challenges, and the current state of evaluation, and proposes an innovative evaluation benchmark designed to provide a holistic perspective on their performance. Additionally, we see the impact of fine-tuning such an LLM, evaluate using our benchmark, and then propose a framework for evaluation of both the agents and their underlying LLMs. The existing frameworks for evaluating LLMs and autonomous agents focus on single tasks and are limited in capturing their capabilities. We outline the methodology for evaluating autonomous agents' performance in responding to single and multi-step prompts. The process consists of three key stages: Preparation of the data, Preparation of the Gold Answers, and Evaluations. We use the meticulously crafted 20 unique prompts to challenge the agents, covering simple and complex questions. Using GPT-4, a state-of-the-art model, we generate the initial responses, which undergo rigorous verification to produce gold answers, indicating correctness and revealing the minimum steps required for task completion. Our evaluation framework relies on two critical metrics: the effort metrics, quantifying the steps taken by autonomous agents, and the success rate, measuring their accuracy in achieving task objectives and also keeping track of hallucinations of the model. We conduct experiments with ten different models, representing the current landscape of natural language processing models, presenting each with 20 unique prompts. Their responses are meticulously compared to our gold answers and gold steps (optimal number of steps) to generate the evaluation metrics. Similarly, a fine-tuned model is also evaluated with ten different questions, which test the agent's decision-making process by selecting the correct tool and then the ability of the model to reach the correct conclusion to the question asked by the user in this process.This comprehensive approach ensures a thorough assessment of autonomous agents' capabilities. It demonstrates the utility of these metrics, revealing how they can shed light on the strengths and weaknesses of various autonomous agents. As a step toward standardization, we propose transforming the evaluation process of LLMs into an automated framework that accommodates all types of language models, agents, and LLM-based applications. Such an approach promises to establish a unified and comprehensive evaluation methodology, empowering users to make informed decisions when selecting, fine-tuning, and assessing the accuracy of underlying language models and their applications for different domains.In summary, this paper contributes to the ongoing research on evaluating LLMs and autonomous agents by introducing a novel benchmark and proposing a framework, focusing on evaluating the language models while keeping different knowledge domains in mind. Our framework will enhance our understanding of these technologies and serve as a valuable resource for researchers, engineers, and practitioners working in the ever-evolving landscape of NLP and autonomous systems.

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Berichte der Organisationen zum Thema "Agents and autonomous systems"

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

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

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

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Chen, Guang. Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects. SAE International, 2023. http://dx.doi.org/10.4271/epr2023017.

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<div class="section abstract"><div class="htmlview paragraph">This report delves into the field of multi-agent collaborative perception (MCP) for autonomous driving: an area that remains unresolved. Current single-agent perception systems suffer from limitations, such as occlusion and sparse sensor observation at a far distance.</div><div class="htmlview paragraph"><b>Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects</b> addresses three unsettled topics that demand immediate attention: <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Establishing normative communication protocols to facilitate seamless information sharing among vehicles</div></li><li class="list-item"><div class="htmlview paragraph">Definiting collaboration strategies, including identifying specific collaboration projects, partners, and content, as well as establishing the integration mechanism</div></li><li class="list-item"><div class="htmlview paragraph">Collecting sufficient data for MCP model training, including capturing diverse modal data and labeling various downstream tasks as accurately as possible</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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Smith, Clint, Andmorgan Fischer, Alex Ly, and Michael Anderson. Autonomous QUerying and PATHogen threat agent sensor system (AQUA PATH) : monitoring source waters with geospatially wirelessly networked distributed sensing systems. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/37533.

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Occhiali, Giovanni, and Fredrick Kalyango. Can Tax Agents Support Tax Compliance in Low-Income Countries? A Review of the Literature and some Preliminary Evidence from Uganda. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/ictd.2021.018.

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Since the late 1970s, many countries have based their tax systems on self-assessment – taxpayers are expected to evaluate their liabilities autonomously, and voluntarily remit their tax due. If the tax system is perceived as fair and easy to navigate, with credible threat of penalisation for non-compliance, self-assessment reduces the cost of tax administration without significant revenue losses (Barr et al. 1977; Teviotdale and Thompson 1999; James and Alley 2004). On the other hand, self-assessment entails an increase in compliance costs for taxpayers, at the very least in terms of time spent complying with their obligations. However, none of the conditions mentioned above – fairness, simplicity and credibility – is easy to meet. Hence, initial moves towards self-assessment were met in many countries with an increased focus on what type of deterrence measures would increase taxpayer compliance (Forest and Sheffrin 2002), following the prevalent theoretical approach of the time (Allingham and Sandmo 1972). By the late 1990s, the focus was shifting to the perceived fairness and complexity of the tax system, increasingly seen as both a direct and indirect obstacle to compliance (Slemrod and Venkatesh 2002; Forest and Sheffrin 2002; Eichfelder and Schorn 2012). Intuitively, a taxpayer who does not understand their tax obligations has a hard time complying with them, and might well decide not to try at all – especially if penalisation is seen as unlikely.

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

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

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

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DeLoach, Scott A. Autonomous Adaptive Information Systems. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada445091.

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Sorrell, F. Y. Autonomous Underwater Vehicle Systems. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada627926.

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