Relevant bibliographies by topics / Agent

Academic literature on the topic 'Agent'

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Published: 4 June 2021
Last updated: 27 July 2024

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Journal articles on the topic "Agent"

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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 (December 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 (April 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 (September 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 (September 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 (January 27, 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 systems. In particular, we discuss both veteran (still maintained) and novel agent programming languages, their extensions, work on comparing some of these languages, and applications found in the literature that make use of agent programming.
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Paetzke, Hans-Henning. "Agent der Menschlichkeit." osteuropa 69, no. 12 (2019): 139–56. http://dx.doi.org/10.35998/oe-2019-0060.

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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 (September 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 definitions and facilities in the SAFER architecture, the formalities of agent fabrication are elaborated. In order to enhance the security of agent-based e-commerce, an infrastructure of agent authorization and authentication is integrated in agent fabrication. Implementation and prototype applications show that the proposed agent fabrication scheme brings forth a potential solution for creating agents in agent-based e-commerce applications.
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Lou, Xingzhou, Junge Zhang, Timothy J. Norman, Kaiqi Huang, and Yali Du. "TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 2024): 17496–504. http://dx.doi.org/10.1609/aaai.v38i16.29699.

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Multi-Agent Policy Gradient (MAPG) has made significant progress in recent years. However, centralized critics in state-of-the-art MAPG methods still face the centralized-decentralized mismatch (CDM) issue, which means sub-optimal actions by some agents will affect other agent's policy learning. While using individual critics for policy updates can avoid this issue, they severely limit cooperation among agents. To address this issue, we propose an agent topology framework, which decides whether other agents should be considered in policy gradient and achieves compromise between facilitating cooperation and alleviating the CDM issue. The agent topology allows agents to use coalition utility as learning objective instead of global utility by centralized critics or local utility by individual critics. To constitute the agent topology, various models are studied. We propose Topology-based multi-Agent Policy gradiEnt (TAPE) for both stochastic and deterministic MAPG methods. We prove the policy improvement theorem for stochastic TAPE and give a theoretical explanation for the improved cooperation among agents. Experiment results on several benchmarks show the agent topology is able to facilitate agent cooperation and alleviate CDM issue respectively to improve performance of TAPE. Finally, multiple ablation studies and a heuristic graph search algorithm are devised to show the efficacy of the agent topology.
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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 (August 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 communication and agent migration. The facilitator agent model, as well as the mobile agent model, is based on agent-oriented G-nets — a high level Petri net formalism. To illustrate our formal modeling technique for mobile agent systems, we provide an example of agent migration to show how a design error can be detected.
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Flathmann, Christopher, Nathan McNeese, and Lorenzo Barberis Canonico. "Using Human-Agent Teams to Purposefully Design Multi-Agent Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1425–29. http://dx.doi.org/10.1177/1071181319631238.

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With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.
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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 modeled and in the situations where there are no such descripctions nor models yet . In this part I also want to introduce my theory of agent-process, which is the possibility of looking at processes as autonomous agents. This theory is presented in practical demonstrations and examples by using the TROPOS methodology, and this theory is supported by references from the areas of agent and business process modeling.
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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 the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web
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 the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web
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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, morphogenesis denotes the development of shapes and form, for example embryos, organs or limbs. Here, I will introduce methods to quantitatively investigate shape formation in stochastic particle systems. In living organisms, self-organization, like the development of an embryo, is a guided process, predetermined by the genetic code, but executed in an autonomous decentralized fashion. Information is processed by the individual agents (e.g. cells) engaged in this process. Hence, information theory can be deployed to study such processes and connect self-organization and information processing. The existing concepts of observer based self-organization and relevant information will be used to devise a framework for the investigation of guided spatial self-organization. Furthermore, local information transfer plays an important role for processes of self-organization. In this context, the concept of synergy has been getting a lot attention lately. Synergy is a formalization of the idea that for some systems the whole is more than the sum of its parts and it is assumed that it plays an important role in self-organization, learning and decision making processes. In this thesis, a novel measure of synergy will be introduced, that addresses some of the theoretical problems that earlier approaches posed.
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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 the mobile agents may behave maliciously and thus act in order to prevent the other agents to complete their goal. We study the Rendezvous problem,i.e., the problem of gathering a set of mobile agents at a single node of a network, starting from a initial scattered configuration and despite the presence of a Malicious agent, and in particular, we propose a novel solution in the bidirectional hypercube network.
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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.
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 situations. Ainsi, nous présentons d'abord un algorithme d'interprétation sémantique de la commande indépendant du type d'interaction (humain-agent ou agent-agent). Cet algorithme considère le rapport entre « ce qui est compris » de la commande et « ce qui est possible » pour la machine. Ce rapport intervient dans un système de sélection de réponses basé sur une mesure de degré de relation sémantique. Nous proposons ensuite une telle mesure, conçue pour prendre en compte plus d'informations que la plupart des mesures actuelles.
Nous étudions ensuite les implémentations que nous avons faites dans les cadres humain-agent et agent-agent. Pour l'implémentation humain-agent, l'une des spécificités est l'utilisation d'une langue naturelle, impliquant le besoin d'utiliser des outils de modélisation de la langue. Pour l'implémentation agent-agent, nous proposerons une adaptation de notre architecture, en s'appuyant sur des protocoles d'interactions entre agents.
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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 to choose between. The number of agents is large, making crowd control an important factor. The simulation is run with a number of numerical algorithms such as path planning and logical reasoning. The algorithms are programmed into a simulation program allowing the evacuation to be shown in real time. The results shows the importance of agents communicating, and clear paths to emergency 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 Agent- systems usually benefit from the following: asynchronous execution, dynamic adaptation, fault-tolerance improvement in network latency, protocol encapsulation, reduction in network load and robustness. However, one of the major technical obstacles to a wider acceptance of the mobile agent is security which is the modus operandi to protect the mobile agents against malicious hosts. This work proposes how the Mobile Agents (MA), supported by a new solid models (detection and protection), can present a new way of securing mobile agents against malicious hosts. The work contributes in proposing a new computing model for protection against malicious hosts. This model is based on trust, which is a combination of two kinds of trust: policy enforcement and control and punishment. The originality of this model is the introduction of the concept of setting up an active storage element in the agent space, called as "home away from home", for partial result storage and separation as well as digital signing of the destination of the mobile agent. An efficient flooding detection scheme is developed by integrating the sketch technique with the Divergence Measures (Hellinger Distance, Chi-Square and Power Divergences). This type of integration can be considered unique in comparison with existing solutions over a Mobile Agent network. The sketch data- structure summarizes the mobile agent's process of calls generating into a fixed set of data for developing a probability model. The Divergence Measures techniques, combined with a Mobile Agent traffic, efficiently identifies attacks, by monitoring the distance between current traffic distribution and the estimated distribution, based on history information. Compared to the previous detection system and existing works, the proposed techniques achieve the advantages of higher accuracy and flexibility, to deal with low intensity attacks and the ability to track the period of attack. Simulation results are presented to demonstrate the performance of the proposed detection model. This work achieves in outperforming the existing detection solutions by tuning the Divergence Measures. An evaluation of the scheme is done via the receiver-operating characteristic (ROC). The work achieves in outperforming the existing detection solutions by tuning the Power Divergence with a value of β=2.2. With this value of β, the detection scheme leads to a very attractive performance in terms of True Positive Rate (100%), False Positive Rate (3.8%) and is capable of detecting low intensity attacks. Moreover, the Power Divergence with β=2.2 presents a better detection accuracy of 98.1% in comparison with Hellinger Distance (60%) and Chi-square (80%). Since the scenarios in consideration in this work can be reasonably related to any type of network, the strength of the proposed model can alternatively be applied to any enterprise network.
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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. Berlin, Heidelberg: 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. Berlin: Springer, 2003.

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Alonso, Eduardo, Daniel Kudenko, and Dimitar Kazakov, eds. Adaptive Agents and Multi-Agent Systems. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Berlin: 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, 307–14. Berlin, Heidelberg: 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, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Agent." In Encyclopedia of Machine Learning, 35. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_13.

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Weik, Martin H. "agent." In Computer Science and Communications Dictionary, 33. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_417.

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Rennie, Frank, and Keith Smyth. "Agent." In Digital Learning: The Key Concepts, 11–12. 2nd ed. London: Routledge, 2019. http://dx.doi.org/10.4324/9780429425240-6.

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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, 157–81. Berlin, Heidelberg: 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, 57–88. Singapore: 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, 105–19. Berlin, Heidelberg: 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, 102–27. Berlin, Heidelberg: 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, 139–52. Berlin, Heidelberg: 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, 340–47. Berlin, Heidelberg: 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 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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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}. California: 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 approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.
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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}. California: 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 high-level specification of the tasks performed by an agent in the form of a reward machine (RM) rather than on its (assumed) program. We show how a reward machine can be used to generate tree and rollout policies for an MCTS-based scheduler. We evaluate our approach in a range of multi-agent environments, and show that RM-based scheduling out-performs previous intention-aware scheduling approaches in settings where agents are not co-designed
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Chen, Ruiqing, Xiaoyuan Zhang, Yali Du, Yifan Zhong, Zheng Tian, Fanglei Sun, and Yaodong Yang. "Off-Agent Trust Region Policy Optimization." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: 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 transfer may result in negative consequences. Moreover, even the experience of homogeneous agents requires modification before reusing. Our approach introduces off-agent adaptations to the multi-agent policy optimization methods, enabling effective and purposeful leverage of cross-agent experiences beyond conventional parameter sharing. Accompanying this, we provide a theoretical guarantee for an approximate monotonic improvement. Experiments conducted on the StarCraftII Multi-Agent Challenge (SMAC) and Google Research Football (GRF) demonstrate that our algorithms outperform state-of-the-art (SOTA) methods and achieve faster convergence, suggesting the viability of our approach for efficient experience reusing in MARL.
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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}. California: 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 training efficiency, while the combination of different modules brings rich diversity. The agent routing at different time steps is learned end-to-end to achieve a dynamic and adaptive balance. Specifically, we also propose an information-theoretical regularization between the routing of agents and their behavior to further guarantee the identifiability of different routing. We evaluated ADMN in challenging StarCraft micromanagement games and Google Research Football games, and results demonstrate the superior performance of ADMN, particularly in larger or heterogeneous cooperative tasks.
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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}. California: 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 the seventeen principles.
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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 order of execution of agents over time. This order of execution is affected by techniques employed by frameworks to simulate the concurrent activities of multiple agents. One such technique is multi-threading. Multi-threaded operating systems, or programming languages and environments, such as Java, allow multiple agents, represented by software threads, to share the computer’s execution time by taking turns, thus appearing to act simultaneously. The precise order of execution of peer threads in multi-threaded applications is often out of the hands of the programmer, and may be determined exclusively by the operating system or program execution environment. However, if overlooked by the framework developer, the idiosyncrasies of a particular thread ordering mechanism can pass on to the modeler inherent random behavior that is neither intuitive, nor in line with the modeler’s expectations. To be considered fair, the engine should aim to provide all agents with equal probability of executing first within a time step, or last, or in any position in between. This paper analyzes the sequencing of agent thread execution within a Java framework that implements a multi-threaded, time-stepping, agent based simulation engine. The natural ordering of Java thread execution is demonstrated to be unfair (that is, not uniform) in its treatment of agents. This research shows that the standard mechanism of Java thread scheduling, while appropriate for most applications, is inappropriate on its own for the agent based framework. It is demonstrated that with Java’s standard thread scheduling algorithm, over time certain agents tend to execute ahead of others within each time step, while others tend to execute in the middle or at the back of the pack. This paper then introduces and demonstrates the “Uniform Specific Notification” pattern, a technique that produces a fairer, uniformly distributed random order for the initial execution of Java agent threads at each simulation time step.
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Chen, Guangyao, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje Karlsson, Jie Fu, and Yemin Shi. "AutoAgents: A Framework for Automatic Agent Generation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: 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 agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents.
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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}. California: 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 analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.
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Okumura, Keisuke, François Bonnet, Yasumasa Tamura, and Xavier Défago. "Offline Time-Independent Multi-Agent Path Planning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/645.

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This paper studies a novel planning problem for multiple agents that cannot share holding resources, named OTIMAPP (Offline Time-Independent Multi-Agent Path Planning). Given a graph and a set of start-goal pairs, the problem consists in assigning a path to each agent such that every agent eventually reaches their goal without blocking each other, regardless of how the agents are being scheduled at runtime. The motivation stems from the nature of distributed environments that agents take actions fully asynchronous and have no knowledge about those exact timings of other actors. We present solution conditions, computational complexity, solvers, and robotic applications.
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Reports on the topic "Agent"

1

Daniele, M., B. Wijnen, and D. Francisco. Agent Extensibility (AgentX) Protocol Version 1. RFC Editor, January 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, January 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.), April 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 enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.
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Cohen, Philip R. Robust Agent-Based Systems Incorporating Teams of Communicating Agents. Fort Belvoir, VA: Defense Technical Information Center, February 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:. Gaithersburg, MD: 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. Fort Belvoir, VA: Defense Technical Information Center, January 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. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada425102.

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8

Kramer, Mitch. IntelliResponse Virtual Agent. Boston, MA: Patricia Seybold Group, May 2013. http://dx.doi.org/10.1571/pr05-10-13cc.

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

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Waugh, Michael. Heterogeneous Agent Trade. Cambridge, MA: National Bureau of Economic Research, October 2023. http://dx.doi.org/10.3386/w31810.

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