Relevant bibliographies by topics / Agent

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Agent'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Agent"

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Aprilia, Dita, Yusrizal Yusrizal, and Wahyu Syarvina. "Analisis Tingkat Literasi Keuangan Agen Asuransi Syariah di PT. Bumiputera 1912 Medan." Jurnal Manajemen Akuntansi (JUMSI) 3, no. 1 (2023): 303–16. http://dx.doi.org/10.36987/jumsi.v3i1.4026.

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This study aims to determine and analyze agent knowledge, agent behavior and agent attitudes towards sharia insurance at PT Bumiputera 1912 Medan. The research method used is a qualitative method used by researchers, namely primary, including distributing questionnaires to interview agents at PT Bumiputera 1912 Medan. Based on the research results obtained regarding the analysis of the level of financial literacy of Islamic insurance agents, namely, even though the knowledge level of Islamic insurance agents is in the moderate category or sufficiently understanding, the knowledge level of Isla
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Gómez-Sanz, Jorge, and Juan Pavón. "Methodologies for Developing Multi-Agent Systems." JUCS - Journal of Universal Computer Science 10, no. (4) (2004): 359–74. https://doi.org/10.3217/jucs-010-04-0359.

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As agent technology has matured with the deployment of a variety of applications, particularly in open and dynamic environments such as the web, several methodologies and tools have been proposed to support software engineers during the development process of such systems. This article takes an overall look at representative agent-oriented methodologies by considering how they support specific agent-related concepts. This serves to identify areas in which this technology has shown its potential to solve new problems, e.g., the ability to manage complexity with an organizational perspective, go
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SUMANTH REDDY, SHIVA. "AI Agents: Agent GPT." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50557.

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Abstract - Agent GPT is an advanced autonomous AI system designed to simulate human-like reasoning and task execution through the deployment of AI agents. Unlike traditional large language models that respond passively to user prompts, Agent GPT can plan, iterate, and execute multi-step goals with minimal human intervention. Each agent operates based on a defined objective, breaking it down into smaller tasks, leveraging APIs, tools, or internet access to gather information, and adapting dynamically to changing conditions. The system is often built on top of language models such as GPT-4, and
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ANDREWS, RUSSELL J. "Neuroprotective "Agents" in Surgery: Secret "Agent" Man, or Common "Agent" Machine?" Annals of the New York Academy of Sciences 890, no. 1 NEUROPROTECTI (1999): 59–72. http://dx.doi.org/10.1111/j.1749-6632.1999.tb07981.x.

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SCHWEITZER, FRANK, and MATTHEW E. TAYLOR. "EDITORIAL: AGENTS AND MULTI-AGENT SYSTEMS." Advances in Complex Systems 14, no. 02 (2011): iii—iv. http://dx.doi.org/10.1142/s0219525911003025.

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Houghton, P. "16 Targeted agent with targeted agents." European Journal of Cancer Supplements 2, no. 8 (2004): 8–9. http://dx.doi.org/10.1016/s1359-6349(04)80025-9.

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Plaza, Enric, Josep Llu�s Arcos, Pablo Noriega, and Carles Sierra. "Competing agents in agent-mediated institutions." Personal Technologies 2, no. 3 (1998): 212–20. http://dx.doi.org/10.1007/bf01321177.

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Cardoso, Rafael C., and Angelo Ferrando. "A Review of Agent-Based Programming for Multi-Agent Systems." Computers 10, no. 2 (2021): 16. http://dx.doi.org/10.3390/computers10020016.

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Intelligent and autonomous agents is a subarea of symbolic artificial intelligence where these agents decide, either reactively or proactively, upon a course of action by reasoning about the information that is available about the world (including the environment, the agent itself, and other agents). It encompasses a multitude of techniques, such as negotiation protocols, agent simulation, multi-agent argumentation, multi-agent planning, and many others. In this paper, we focus on agent programming and we provide a systematic review of the literature in agent-based programming for multi-agent
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XU, HAIPING, ZHIGUO ZHANG, and SOL M. SHATZ. "A SECURITY BASED MODEL FOR MOBILE AGENT SOFTWARE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 15, no. 04 (2005): 719–46. http://dx.doi.org/10.1142/s0218194005002518.

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Security modeling for agents has been one of the most challenging issues in developing practical mobile agent software systems. In the past, researchers have developed mobile agent systems with emphasis either on protecting mobile agents from malicious hosts or protecting hosts from malicious agents. In this paper, we propose a security based mobile agent system architecture that provides a general solution to protecting both mobile agents and agent hosts in terms of agent communication and agent migration. We present a facilitator agent model that serves as a middleware for secure agent commu
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GUAN, SHENG-UEI, and FANGMING ZHU. "AGENT FABRICATION AND ITS IMPLEMENTATION FOR AGENT-BASED ELECTRONIC COMMERCE." International Journal of Information Technology & Decision Making 01, no. 03 (2002): 473–89. http://dx.doi.org/10.1142/s0219622002000300.

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In the last decade, agent-based e-commerce has emerged as a potential role for the next generation of e-commerce. How to create agents for e-commerce applications has become a serious consideration in this field. This paper proposes a new scheme named agent fabrication and elaborates its implementation in multi-agent systems based on the SAFER (Secure Agent Fabrication, Evolution and Roaming) architecture. First, a conceptual structure is proposed for software agents carrying out e-commerce activities. Furthermore, agent module suitcase is defined to facilitate agent fabrication. With these de
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Dissertations / Theses on the topic "Agent"

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Doskočilová, Veronika. "Využití agentů v business procesech." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-124783.

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This work deals with the possibilities of using agents and multiagent systems in the areas of business process management and business process modeling. The aim of the theoretical part is to describe the theory of artificial agents, to assess the benefits of this approach and to describe current applications of MAS in BPM. In the theoretical part I also describe the issue of management and business process modeling and methodology MMABP. The aim of the analytical part is to summarize the possibilities of using MAS in BPM in the situations where business processes are already described and mode
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Lau, Pik Lik Billy. "Interdependence between agents in multi agent systems." Thesis, Curtin University, 2014. http://hdl.handle.net/20.500.11937/439.

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Interdependence relationships have defined the foundation of cooperation between agents but limited by existing protocols. As a consequence, the idle agents are not able to join and benefit through it. First, the inter-relationship has been studied and certified for the purpose of securing mutual gains. Next, the join coalition mechanism is proposed to help idle agents to join existing macroscopic and microscopic coalitions which are based on the goals compatibilities, budget and trust
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Dinu, Razvan. "Web Agents : towards online hybrid multi-agent systems." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20126/document.

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Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have th
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Harder, Malte. "Information driven self-organization of agents and agent collectives." Thesis, University of Hertfordshire, 2014. http://hdl.handle.net/2299/13907.

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From a visual standpoint it is often easy to point out whether a system is considered to be self-organizing or not, though a quantitative approach would be more helpful. Information theory, as introduced by Shannon, provides the right tools not only quantify self-organization, but also to investigate it in relation to the information processing performed by individual agents within a collective. This thesis sets out to introduce methods to quantify spatial self-organization in collective systems in the continuous domain as a means to investigate morphogenetic processes. In biology, morphogenes
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Vanzan, Alessio <1993&gt. "Mobile Agents Rendezvous in Networks Despite a Malicious Agent." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10604.

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The use of mobile agents, i.e., autonomous software entities , finds its application in many settings such as Network Maintenance, Electronic commerce and Intelligent search. Given a network, we have a set of mobile agents that moves from node to node in order to achieve a common task. Security is an important issue that can arise in such environment and that has been widely studied in literature. In particular, it is important to study the problem of having a host or an agent that hinders or harms the honest agents in their tasks. In this thesis we consider a scenario in which one of t
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Mazuel, Laurent. "Traitement de l'hétérogénéité sémantique dans les interactions humain-agent et agent-agent." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2008. http://tel.archives-ouvertes.fr/tel-00413004.

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Le thème général de cette thèse est le traitement de l'hétérogénéité sémantique dans les interactions humain-agent et agent-agent. Plus précisément, nous étudions le cas où un agent informatique muni d'un modèle de représentation de ses connaissances doit traiter des demandes envoyées par d'autres interlocuteurs, qu'il s'agisse d'utilisateurs humains ou d'agents informatiques.<br />La plupart des approches segmentent ce traitement en fonction de l'émetteur de la demande (humain ou agent). Nous pensons au contraire qu'il est possible de proposer un modèle d'interaction commun aux deux situation
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Bergh, Niklas. "An autonomous multi-agent evacuation scenario using sight and agent-to-agent communication." Thesis, KTH, Optimeringslära och systemteori, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147930.

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This report simulates an evacuation scenario in a crowded building and attempts to optimize the flow of agents during the process. Each agent is autonomous and assumed to know the map. Agents also have the ability to communicate between themselves, as well as using sight to perceive the environment around them. The purpose is to design a set of rules making the agents evacuate efficiently, which then can applied in real world situation for training people in evacuation strategies. The model used is a time and space discrete setting, where agents move in a discrete graph, and have several exits
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Nagi, Khaled [Verfasser]. "Transactional agents : towards a robust multi-agent system / K. Nagi." Berlin, 2001. http://d-nb.info/965521001/34.

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Tajer, Jean. "Detection of malicious hosts against agents in Mobile Agent networks." Thesis, University of Portsmouth, 2018. https://researchportal.port.ac.uk/portal/en/theses/detection-of-malicious-hosts-against-agents-in-mobile-agent-networks(beca3871-a989-4137-9a4d-13c88d8893d9).html.

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Over the last decade, networks have become increasingly advanced in terms of size, complexity and the level of heterogeneity, due to increase of number of users, devices and implementation of cloud among big enterprises and developing smart cities. As networks become more complicated, the existing client-server paradigm suffers from problems such as delay, jitter, bad quality of service, insufficient scalability, availability and flexibility. The appearance of mobile agents' technology is getting popular as means for an efficient way to access remote resources on computer networks. Mobile Agen
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Huerta, Jose Manuel. "Attitudes of county Extension agents toward agent specialization in Ohio /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487846354481973.

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Books on the topic "Agent"

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Wagner, Tom, and Omer F. Rana, eds. Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47772-1.

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1967-, Alonso Eduardo, Kudenko Daniel 1968-, and Kazakov Dimitar 1967-, eds. Adaptive agents and multi-agent systems, adaptation and multi-agent learning. Springer, 2003.

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Alonso, Eduardo, Daniel Kudenko, and Dimitar Kazakov, eds. Adaptive Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44826-8.

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Bui, The Duy, Tuong Vinh Ho, and Quang Thuy Ha, eds. Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6.

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Kuwabara, Kazuhiro, and Jaeho Lee, eds. Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45680-5.

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Barley, Michael Wayne, and Nik Kasabov, eds. Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b107183.

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Kotz, David, and Friedemann Mattern, eds. Agent Systems, Mobile Agents, and Applications. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/b75241.

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Lee, Jaeho, and Mike Barley, eds. Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b94219.

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Tuyls, Karl, Ann Nowe, Zahia Guessoum, and Daniel Kudenko, eds. Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77949-0.

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1968-, Kudenko Daniel, Kazakov Dimitar 1967-, Alonso Eduardo 1967-, and LINK (Online service), eds. Adaptive agents and multi-agent systems III: Adaptation and multi-agent learning. Springer, 2005.

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Book chapters on the topic "Agent"

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Duc, Nguyen Tuan, and Ikuo Takeuchi. "Abstraction of Agent Cooperation in Agent Oriented Programming Language." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_34.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, et al. "Agent." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_13.

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Rennie, Frank, and Keith Smyth. "Agent." In Digital Learning: The Key Concepts, 2nd ed. Routledge, 2019. http://dx.doi.org/10.4324/9780429425240-6.

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Weik, Martin H. "agent." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_417.

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Lettmann, Theodor, Michael Baumann, Markus Eberling, and Thomas Kemmerich. "Modeling Agents and Agent Systems." In Transactions on Computational Collective Intelligence V. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24016-4_9.

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Rakshit, Pratyusha, and Amit Konar. "Agents and Multi-agent Coordination." In Cognitive Intelligence and Robotics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8642-7_2.

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Gleizes, Marie-Pierre, Valérie Camps, Anthony Karageorgos, and Giovanna Di Marzo Serugendo. "Agents and Multi-Agent Systems." In Natural Computing Series. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17348-6_5.

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Vincent, Regis, Bryan Horling, and Victor Lesser. "An Agent Infrastructure to Build and Evaluate Multi-Agent Systems: The Java Agent Framework and Multi-Agent System Simulator." In Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47772-1_11.

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Dastani, Mehdi, Christian P. Mol, and Bas R. Steunebrink. "Modularity in Agent Programming Languages." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_17.

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Pike, Janine Claire, and Elizabeth Marie Ehlers. "Revenue Maximising Adaptive Auctioneer Agent." In Intelligent Agents and Multi-Agent Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_38.

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Conference papers on the topic "Agent"

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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 s
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Dann, Michael, Yuan Yao, Natasha Alechina, Brian Logan, Felipe Meneguzzi, and John Thangarajah. "Multi-Agent Intention Recognition and Progression." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/11.

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For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our
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Tešmanović, Stefan. "Agent treniran reinforcement learning-om za Tribes igricu." In 17th Student Project Conference. University of Nis, Faculty of Electronic Engineering, 2024. https://doi.org/10.46793/ieeestec17.371t.

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Ovaj rad se bavi implementacijom agenta zasnovanog na reinforcement learning-u za igru Tribes. Igra Tribes je strateška igra zasnovana na potezima, gde je cilj prikupiti više poena od protivnika ili osvojiti njegov glavni grad. Agent koristi neuronsku mrežu treniranu pomoću Reinforce algoritma za donošenje odluka o kretanju i akcijama trupa, dok se za akcije gradova i plemena oslanja na unapređenu funkciju nagrade iz referentnog rada, koji se bavio kreiranjem igre i implementacijom agenata zasnovanih na simuliranju poteza. Tokom treninga, agent igra protiv manje istrenirane verzije sebe kako b
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Dann, Michael, Yuan Yao, Natasha Alechina, Brian Logan, and John Thangarajah. "Multi-Agent Intention Progression with Reward Machines." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/31.

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Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume that the programs of other agents are known, or are "similar" to that of the agent making the prediction. While this assumption is reasonable in some circumstances, it is less plausible when the agents are not co-designed. In this paper, we present a new approach to multi-agent intention scheduling in which agents predict the actions of other agents based on a h
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Yu, Yang, Qiyue Yin, Junge Zhang, Pei Xu, and Kaiqi Huang. "ADMN: Agent-Driven Modular Network for Dynamic Parameter Sharing in Cooperative Multi-Agent Reinforcement Learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/34.

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Parameter sharing is a common strategy in multi-agent reinforcement learning (MARL) to make the training more efficient and scalable. However, applying parameter sharing among agents indiscriminately hinders the emergence of agents diversity and degrades the final cooperative performance. To better balance parameter sharing and agents diversity, we propose a novel Agent-Driven Modular Network (ADMN), where agents share a base network consisting of multiple specialized modules, and each agent has its own routing to connect these modules. In ADMN, modules are shared among agents to improve the t
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Chen, Ruiqing, Xiaoyuan Zhang, Yali Du, et al. "Off-Agent Trust Region Policy Optimization." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/420.

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Leveraging the experiences of other agents offers a powerful mechanism to enhance policy optimization in multi-agent reinforcement learning (MARL). However, contemporary MARL algorithms often neglect experience sharing possibilities or adopt a simple approach via direct parameter sharing. Our work explores a refined off-agent learning framework that allows selective integration of experience from other agents to improve policy learning. Our investigation begins with a thorough assessment of current mechanisms for reusing experiences among heterogeneous agents, revealing that direct experience
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Yu, Liuwen, Dongheng Chen, Lisha Qiao, Yiqi Shen, and Leendert van der Torre. "A Principle-based Analysis of Abstract Agent Argumentation Semantics." In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/60.

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Abstract agent argumentation frameworks extend Dung’s theory with agents, and in this paper we study four types of semantics for them. First, agent defense semantics replaces Dung’s notion of defense by some kind of agent defense. Second, social agent semantics prefers arguments that belong to more agents. Third, agent reduction semantics considers the perspective of individual agents. Fourth, agent filtering semantics are inspired by a lack of knowledge. We study five existing principles and we introduce twelve new ones. In total, we provide a full analysis of fifty-two agent semantics and th
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Welch, Lawrence, and Stephen Ekwaro-Osire. "Fairness in Agent Based Simulation Frameworks." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49326.

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An agent based simulation engine should provide a fair playing field for all of its agents. A fundamental design axiom of agent based simulation frameworks is that the simulation engine should not arbitrarily bias its execution towards one agent or another. This fairness is basic to giving the agent modeler confidence that differences in behavior and performance between agents derive legitimately from the simulation modeling, initial conditions or specific agent characteristics, rather than the capriciousness of the underlying framework. One aspect of fairness in a simulation is the relative o
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Chen, Guangyao, Siwei Dong, Yu Shu, et al. "AutoAgents: A Framework for Automatic Agent Generation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/3.

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Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required age
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Andreychuk, Anton, Konstantin Yakovlev, Dor Atzmon, and Roni Stern. "Multi-Agent Pathfinding with Continuous Time." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/6.

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Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grids, assumed agents' actions have uniform duration, and that time is discretized into timesteps. In this work, we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of Safe Interval Path Planning (SIPP), a continuous time single agent planning algorithms, and Conflict-Based Search (CBS). We a
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Reports on the topic "Agent"

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Daniele, M., B. Wijnen, and D. Francisco. Agent Extensibility (AgentX) Protocol Version 1. RFC Editor, 1998. http://dx.doi.org/10.17487/rfc2257.

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Daniele, M., B. Wijnen, M. Ellison, and D. Francisco. Agent Extensibility (AgentX) Protocol Version 1. RFC Editor, 2000. http://dx.doi.org/10.17487/rfc2741.

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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancement
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4

Cohen, Philip R. Robust Agent-Based Systems Incorporating Teams of Communicating Agents. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada421753.

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Fong, Elizabeth, Nenad Ivezic, Rick Korchak, Yun Peng, and Thomas Rhodes. Agent technology:. National Institute of Standards and Technology, 2002. http://dx.doi.org/10.6028/nist.ir.6858.

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Kassab, Lora L., and Jeffrey Voas. Agent Trustworthiness. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada465142.

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McDonald, Jeffrey T., Alec Yasinsac, Willard C. Thompson, and III. Mobile Agent Data Integrity Using Multi-Agent Architecture. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada425102.

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Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

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The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
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Kramer, Mitch. IntelliResponse Virtual Agent. Patricia Seybold Group, 2013. http://dx.doi.org/10.1571/pr05-10-13cc.

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Jansen, Wayne, and Tom Karygiannis. Mobile agent security. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.sp.800-19.

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