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Journal articles on the topic 'Agent'

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Published: 4 June 2021
Last updated: 27 July 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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9

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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10

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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11

Sajid, Qandeel, Ryan Luna, and Kostas Bekris. "Multi-Agent Pathfinding with Simultaneous Execution of Single-Agent Primitives." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 88–96. http://dx.doi.org/10.1609/socs.v3i1.18243.

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Multi-agent pathfinding is a challenging combinatorial problem that involves multiple agents moving on a graph from a set of initial nodes to a set of desired goals without inter-agent collisions. Searching the composite space of all agents has exponential complexity and does not scale well. Decoupled methods are more efficient but are generally incomplete. There are, however, polynomial time algorithms, which utilize single or few-agents primitives with completeness guarantees. One limitation of these alternatives is that the resulting solution is sequential, where only one agent moves at a time. Such solutions are of low quality when compared to methods where multiple agents can move simultaneously. This work proposes an algorithm for multi-agent pathfinding that utilizes similar single-agent primitives but allows all agents to move in parallel. The paper describes the algorithm and its properties. Experimental comparisons suggest that the resulting paths are considerably better than sequential ones, even after a post-processing, parallelization step, as well as solutions returned by decoupled and coupled alternatives. The experiments also suggest good scalability and competitive computational performance.
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Ruan, Jingqing, Linghui Meng, Xuantang Xiong, Dengpeng Xing, and Bo Xu. "Learning Multi-Agent Action Coordination via Electing First-Move Agent." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 624–28. http://dx.doi.org/10.1609/icaps.v32i1.19850.

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Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously, and asynchronous action coordination between agents is rarely considered. This paper introduces a bi-level multi-agent decision hierarchy for coordinated behavior planning. We propose a novel election mechanism in which we adopt a graph convolutional network to model the interaction among agents and elect a first-move agent for asynchronous guidance. We also propose a dynamically weighted mixing network to effectively reduce the misestimation of the value function during training. This work is the first to explicitly model the asynchronous multi-agent action coordination, and this explicitness enables to choose the optimal first-move agent. The results on Cooperative Navigation and Google Football demonstrate that the proposed algorithm can achieve superior performance in cooperative environments. Our code is available at https://github.com/Amanda-1997/EFA-DWM.
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Elijah and Muhammad Naveed Dilber. "Complete Analysis of Fault Tolerance Schemes in Mobile Agents for a Reliable Mobile Agent Computation." Bonfring International Journal of Industrial Engineering and Management Science 7, no. 1 (March 31, 2017): 20–24. http://dx.doi.org/10.9756/bijiems.8322.

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14

Rahim, Tengku Nurulhuda Tengku Abd, Ma Stella Tabora Domingo, Mohamed Farid Noor Batcha, and Zalilah Abd Aziz. "Automated Exam Question Set Generator Using Utility Based Agent and Learning Agent." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 164–69. http://dx.doi.org/10.18178/ijmlc.2020.10.1.914.

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15

Matsui, Tetsuya, and Atsushi Koike. "Who Is to Blame? The Appearance of Virtual Agents and the Attribution of Perceived Responsibility." Sensors 21, no. 8 (April 9, 2021): 2646. http://dx.doi.org/10.3390/s21082646.

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Virtual agents have been widely used in human-agent collaboration work. One important problem with human-agent collaboration is the attribution of responsibility as perceived by users. We focused on the relationship between the appearance of a virtual agent and the attribution of perceived responsibility. We conducted an experiment with five agents: an agent without an appearance, a human-like agent, a robot-like agent, a dog-like agent, and an angel-like agent. We measured the perceived agency and experience for each agent, and we conducted an experiment involving a sound-guessing game. In the game, participants listened to a sound and guessed what the sound was with an agent. At the end of the game, the game finished with failure, and the participants did not know who made the mistake, the participant or the agent. After the game, we asked the participants how they perceived the agents’ trustworthiness and to whom they attributed responsibility. As a result, participants attributed less responsibility to themselves when interacting with a robot-like agent than interacting with an angel-like robot. Furthermore, participants perceived the least trustworthiness toward the robot-like agent among all conditions. In addition, the agents’ perceived experience had a correlation with the attribution of perceived responsibility. Furthermore, the agents that made the participants feel their attribution of responsibility to be less were not trusted. These results suggest the relationship between agents’ appearance and perceived attribution of responsibility and new methods for designs in the creation of virtual agents for collaboration work.
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16

Vulis, Michael. "Agent/free agent newsreaders." Complexity 2, no. 2 (November 1996): 47–48. http://dx.doi.org/10.1002/(sici)1099-0526(199611/12)2:2<47::aid-cplx12>3.0.co;2-t.

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17

Sitepu, Novi Indriyani, and Saparuddin Siregar. "IMPROVING THE QUALITY OF SHARIA INSURANCE AGENTS THROUGH TECHNOLOGY OPTIMALIZATION AS A SHARIA-BASED MARKETING MEDIUM." At-tijaroh: Jurnal Ilmu Manajemen dan Bisnis Islam 7, no. 1 (June 30, 2021): 15–29. http://dx.doi.org/10.24952/tijaroh.v7i1.3093.

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The physical, the intellectual and the spiritual qualities of the agent is creates agent competence so that they can compete in the sharia insurance marketing business. Competition for human performance is currently over helmed by the ease of technology through the internet mediawhich poses a challenge for agents to be able to take advantage of technology. The high competition needs to be framed using sharia views in order to maintain Islamic values. This phenomenon then makes insurance agents as a technology driver in marketing the shariah insurance business in sharia companies in the Nort Sumatera. This stydy uses a qualitative method with a construction approach. Primary research data were obtained through interviews with agency company leaders and MDRT agents to found the quality agens. Secondary data were obtained from literature studies. The results showed that the physical quality of Islamic insurance agents is very influential in marketing products but in technology optimizationit has less effect. While intellectual quality has an effect on the ability of agent technology optimization, then spiritual quality to greatly effect the ability to optimize technology. Sharia insurance agents at takaful and prudential companies have optimized technology as their marketing medium frame by sharia views
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18

Bosse, Stefan. "Industrial Agents and Distributed Agent-Based Learning." Proceedings 1, no. 2 (November 15, 2016): 14. http://dx.doi.org/10.3390/casa-3-s2004.

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19

Li, Jiaoyang, Pavel Surynek, Ariel Felner, Hang Ma, T. K. Satish Kumar, and Sven Koenig. "Multi-Agent Path Finding for Large Agents." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7627–34. http://dx.doi.org/10.1609/aaai.v33i01.33017627.

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Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a twolevel tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. Geometric agents are referred to as “large” agents because they can occupy multiple points at the same time. In this paper, we formalize and study LAMAPF, i.e., MAPF for large agents. We first show how CBS can be adapted to solve LA-MAPF. We then present a generalized version of CBS, called Multi-Constraint CBS (MCCBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. We introduce three different approaches to choose such constraints as well as an approach to compute admissible heuristics for the high-level search. Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. The best variant also outperforms EPEA* (a state-of-the-art A*-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.
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20

Chiariglione, Leonardo, Masahiro Shibata, and Yeun-Bae Kim. "Agent Software Technologies. Standardization of Communicating Agents." Journal of the Institute of Image Information and Television Engineers 52, no. 4 (1998): 452–56. http://dx.doi.org/10.3169/itej.52.452.

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21

Hanretty, Chris. "Agents and Patients: The Principal-Agent Perspective." European Political Science 9, no. 4 (December 2010): 481–87. http://dx.doi.org/10.1057/eps.2010.57.

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22

Clarke, Randolph. "Free Will, Agent Causation, and “Disappearing Agents”." Noûs 53, no. 1 (May 12, 2017): 76–96. http://dx.doi.org/10.1111/nous.12206.

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23

Nelson, Kay M., Alex Kogan, Rajendra P. Srivastava, Miklos A. Vasarhelyi, and Hai Lu. "Virtual auditing agents: the EDGAR Agent challenge." Decision Support Systems 28, no. 3 (May 2000): 241–53. http://dx.doi.org/10.1016/s0167-9236(99)00088-3.

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24

Voth, D. "Practical agents help out [virtual agent system." IEEE Intelligent Systems 20, no. 2 (March 2005): 4–6. http://dx.doi.org/10.1109/mis.2005.35.

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25

Li, Jiaoyang, Pavel Surynek, Ariel Felner, Hang Ma, T. K. Kumar, and Sven Koenig. "Multi-Agent Path Finding for Large Agents." Proceedings of the International Symposium on Combinatorial Search 10, no. 1 (September 1, 2021): 186–87. http://dx.doi.org/10.1609/socs.v10i1.18483.

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Multi-Agent Path Finding (MAPF) has been widely studied in the AI community. For example, Conflict-Based Search (CBS) is a state-of-the-art MAPF algorithm based on a two-level tree-search. However, previous MAPF algorithms assume that an agent occupies only a single location at any given time, e.g., a single cell in a grid. This limits their applicability in many real-world domains that have geometric agents in lieu of point agents. Geometric agents are referred to as “large” agents because they can occupy multiple points at the same time. In this paper, we formalize and study LAMAPF, i.e., MAPF for large agents. We first show how CBS can be adapted to solve LA-MAPF. We then present a generalized version of CBS, called Multi-Constraint CBS (MC-CBS), that adds multiple constraints (instead of one constraint) for an agent when it generates a high-level search node. We introduce three different approaches to choose such constraints as well as an approach to compute admissible heuristics for the high-level search. Experimental results show that all MC-CBS variants outperform CBS by up to three orders of magnitude in terms of runtime. The best variant also outperforms EPEA* (a state-of-the-art A*-based MAPF solver) in all cases and MDD-SAT (a state-of-the-art reduction-based MAPF solver) in some cases.
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26

Fan, Dongyu, Haikuo Shen, and Lijing Dong. "Multi-Agent Distributed Deep Deterministic Policy Gradient for Partially Observable Tracking." Actuators 10, no. 10 (October 14, 2021): 268. http://dx.doi.org/10.3390/act10100268.

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In many existing multi-agent reinforcement learning tasks, each agent observes all the other agents from its own perspective. In addition, the training process is centralized, namely the critic of each agent can access the policies of all the agents. This scheme has certain limitations since every single agent can only obtain the information of its neighbor agents due to the communication range in practical applications. Therefore, in this paper, a multi-agent distributed deep deterministic policy gradient (MAD3PG) approach is presented with decentralized actors and distributed critics to realize multi-agent distributed tracking. The distinguishing feature of the proposed framework is that we adopted the multi-agent distributed training with decentralized execution, where each critic only takes the agent’s and the neighbor agents’ policies into account. Experiments were conducted in the distributed tracking tasks based on multi-agent particle environments where N(N=3,N=5) agents track a target agent with partial observation. The results showed that the proposed method achieves a higher reward with a shorter training time compared to other methods, including MADDPG, DDPG, PPO, and DQN. The proposed novel method leads to a more efficient and effective multi-agent tracking.
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27

Sharp, William H., and Marc M. Sebrechts. "Impact of Perceived Agent Expertise on Trust in Computer Agent Recommendations." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1355–59. http://dx.doi.org/10.1177/1071181320641323.

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Computer agents are frequently anthropomorphized, giving them appearances and responses similar to humans. Research has demonstrated that users tend to apply social norms and expectations to such computer agents, and that people interact with computer agents in a similar fashion as they would another human. Perceived expertise has been shown to affect trust in human-human relationships, but the literature investigating how this influences trust in computer agents is limited. The current study investigated the effect of computer agent perceived level of expertise and recommendation reliability on subjective (rated) and objective (compliance) trust during a pattern recognition task. Reliability of agent recommendations had a strong effect on both subjective and objective trust. Expert agents started with higher subjective trust, but showed less trust repair. Agent expertise had little impact on objective trust resiliency or repair.
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28

Ibrahim, Romani Farid. "Agent Mobile Transaction Model." International Journal of Information and Electronics Engineering 7, no. 2 (2017): 48–54. http://dx.doi.org/10.18178/ijiee.2017.7.2.660.

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29

Foster, Jared, Boris Freidlin, E. L. Korn, and Malcolm Smith. "Evaluation of the contribution of randomised cancer clinical trials evaluating agents without documented single-agent activity." ESMO Open 5, no. 5 (October 2020): e000871. http://dx.doi.org/10.1136/esmoopen-2020-000871.

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BackgroundWith the development of targeted agents, the approach to combination cancer therapy has evolved to focus on identifying ways in which pathway inhibition by one agent may enhance the activity of other agents. In theory, this implies that under this new paradigm, agents are no longer required to show single-agent activity, as the pathway inhibited by the targeted agent may only have a therapeutic effect when given with other agents. This raises the question of the extent to which anticancer agents without single-agent activity can contribute to effective combination regimens.Patients and methodsWe reviewed outcomes of randomised phase 2 combination trials sponsored by the National Cancer Institute Cancer Therapy Evaluation Program that were activated in 2008 to 2017 and noted the single-agent activity of the experimental agents.ResultsFifty-three trials were identified, and 50 had available results: 7 (14%), 15 (30%) and 28 (56%) had experimental agents with single-agent activity classified as active, inactive and indeterminate, respectively. Thirteen per cent (95% CI=1.7% to 40.5%) of trials evaluating inactive agents and 11.6% (95% CI=3.9% to 25.1%) of trials evaluating agents without known single-agent activity (pooled inactive and indeterminate) were positive, compared with 42.9% (95% CI=9.9% to 81.6%) for agents with single-agent activity.ConclusionsIncorporating agents without documented single-agent activity into treatment regimens is unlikely to produce meaningful improvements in activity unless there is compelling biological rationale. This finding has important implications for the prioritisation of anticancer agents for combination testing, and for the allocation of clinical trial resources.
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Lavendelis, Egons, and Janis Grundspenkis. "Design of Multi-Agent Based Intelligent Tutoring Systems." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (January 1, 2009): 48–59. http://dx.doi.org/10.2478/v10143-009-0004-z.

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Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.
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Du, Wei, Shifei Ding, Lili Guo, Jian Zhang, and Ling Ding. "Expressive Multi-Agent Communication via Identity-Aware Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 2024): 17354–61. http://dx.doi.org/10.1609/aaai.v38i16.29683.

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Information sharing through communication is essential for tackling complex multi-agent reinforcement learning tasks. Many existing multi-agent communication protocols can be viewed as instances of message passing graph neural networks (GNNs). However, due to the significantly limited expressive ability of the standard GNN method, the agent feature representations remain similar and indistinguishable even though the agents have different neighborhood structures. This further results in the homogenization of agent behaviors and reduces the capability to solve tasks effectively. In this paper, we propose a multi-agent communication protocol via identity-aware learning (IDEAL), which explicitly enhances the distinguishability of agent feature representations to break the diversity bottleneck. Specifically, IDEAL extends existing multi-agent communication protocols by inductively considering the agents' identities during the message passing process. To obtain expressive feature representations for a given agent, IDEAL first extracts the ego network centered around that agent and then performs multiple rounds of heterogeneous message passing, where different parameter sets are applied to the central agent and the other surrounding agents within the ego network. IDEAL fosters expressive communication between agents and generates distinguishable feature representations, which promotes action diversity and individuality emergence. Experimental results on various benchmarks demonstrate IDEAL can be flexibly integrated into various multi-agent communication methods and enhances the corresponding performance.
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BRAZIER, F. M. T., C. M. JONKER, J. TREUR, and N. J. E. WIJNGAARDS. "DELIBERATIVE EVOLUTION IN MULTI-AGENT SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 11, no. 05 (October 2001): 559–81. http://dx.doi.org/10.1142/s0218194001000670.

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Evolution of automated systems, in particular evolution of automated agents based on agent deliberation, is the topic of this paper. Evolution is not a merely material process, it requires interaction within and between individuals, their environments and societies of agents. An architecture for an individual agent capable of (1) deliberation about the creation of new agents, and (2) (run-time) creation of a new agent on the basis of this, is presented. The agent architecture is based on an existing generic agent model, and includes explicit formal conceptual representations of both design structures of agents and (behavioural) properties of agents. The process of deliberation is based on an existing generic reasoning model of design. The architecture has been designed using the compositional development method DESIRE, and has been tested in a prototype implementation.
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33

Dou, Lintao, Zhen Jia, and Jian Huang. "Solving large-scale multi-agent tasks via transfer learning with dynamic state representation." International Journal of Advanced Robotic Systems 20, no. 2 (March 1, 2023): 172988062311624. http://dx.doi.org/10.1177/17298806231162440.

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Many research results have emerged in the past decade regarding multi-agent reinforcement learning. These include the successful application of asynchronous advantage actor-critic, double deep Q-network and other algorithms in multi-agent environments, and the more representative multi-agent training method based on the classical centralized training distributed execution algorithm QMIX. However, in a large-scale multi-agent environment, training becomes a major challenge due to the exponential growth of the state-action space. In this article, we design a training scheme from small-scale multi-agent training to large-scale multi-agent training. We use the transfer learning method to enable the training of large-scale agents to use the knowledge accumulated by training small-scale agents. We achieve policy transfer between tasks with different numbers of agents by designing a new dynamic state representation network, which uses a self-attention mechanism to capture and represent the local observations of agents. The dynamic state representation network makes it possible to expand the policy model from a few agents (4 agents, 10 agents) task to large-scale agents (16 agents, 50 agents) task. Furthermore, we conducted experiments in the famous real-time strategy game Starcraft II and the multi-agent research platform MAgent. And also set unmanned aerial vehicles trajectory planning simulations. Experimental results show that our approach not only reduces the time consumption of a large number of agent training tasks but also improves the final training performance.
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34

Sghir, Ines, Ines Ben Jaafar, and Khaled Ghédira. "A Multi-Agent Based Optimization Method for Combinatorial Optimization Problems." International Journal on Artificial Intelligence Tools 27, no. 05 (August 2018): 1850021. http://dx.doi.org/10.1142/s0218213018500215.

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This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.
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Schmidt, L. H. "The MIC50/MIC90: assessments of in vitro activities of antimicrobial agents that facilitate comparative agent-agent and agent-species susceptibility comparisons." Antimicrobic Newsletter 4, no. 1 (January 1987): 1–8. http://dx.doi.org/10.1016/0738-1751(87)90012-8.

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MÜLLER, JÖRG P. "Architectures and applications of intelligent agents: A survey." Knowledge Engineering Review 13, no. 4 (February 1999): 353–80. http://dx.doi.org/10.1017/s0269888998004020.

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The objective of this paper is twofold. In its first part, we survey the state of the art in research on agent architectures. The architecture of an agent describes its modules and capabilities, and how these operate together. We structure the field by investigating three important research threads, i.e. architectures for reactive agents, deliberative agents and interacting agents. Then we describe various hybrid approaches that reconcile these three threads, aiming at a combination of different features like reactivity, deliberation and the ability to interact with other agents. These approaches are contrasted with architectural issues of recent agent-based work, including software agents, softbots, believable agents, as well as commercial agent-based systems. The second part of the paper addresses software engineers and system designers who are interested in applying agent technology to their problem domains. The objective of this part is to assist these readers in deciding which agent architecture to choose for a specific application. We characterise the most important domains to which the different approaches described in the first part have been applied, propose an application-related taxonomy of agents, and give a set of guidelines to select the right agent (architecture) for a given application.
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Meneses, Alexis, Hamed Mahzoon, Yuichiro Yoshikawa, and Hiroshi Ishiguro. "Multiple Groups of Agents for Increased Movement Interference and Synchronization." Sensors 22, no. 14 (July 21, 2022): 5465. http://dx.doi.org/10.3390/s22145465.

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We examined the influence of groups of agents and the type of avatar on movement interference. In addition, we studied the synchronization of the subject with the agent. For that, we conducted experiments utilizing human subjects to examine the influence of one, two, or three agents, as well as human or robot avatars, and finally, the agent moving biologically or linearly. We found the main effect on movement interference was the number of agents; namely, three agents had significantly more influence on movement interference than one agent. These results suggest that the number of agents is more influential on movement interference than other avatar characteristics. For the synchronization, the main effect of the type of the agent was revealed, showing that the human agent kept more synchronization compared to the robotic agent. In this experiment, we introduced an additional paradigm on the interference which we called synchronization, discovering that a group of agents is able to influence this behavioral level as well.
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Palanca, Javier, Jaime Andres Rincon, Carlos Carrascosa, Vicente Javier Julian, and Andrés Terrasa. "Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE." Electronics 12, no. 3 (January 28, 2023): 659. http://dx.doi.org/10.3390/electronics12030659.

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Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper.
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WANG, Wulan, and Masaaki INAISHI. "Agent Oriented Marine Traffic Simulation : Ship Agents and Port Agents." Journal of Japan Institute of Navigation 122 (2010): 85–90. http://dx.doi.org/10.9749/jin.122.85.

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Yu, Miao. "The Extension of KQML Primitive in Large Database Operation." Applied Mechanics and Materials 263-266 (December 2012): 1515–18. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1515.

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In Multi-Agent system, the communication among the Agents is the basic problem. The Agent communication language design, especially for large database operation, has been the important part for improving the Multi-Agent system performance. Based on the Multi-Agent large scale database operation, this paper extends the KQML's Primitive for Agents communication. The extended KQML's Primitive can improve the Agent communication performance and the database query performance.
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FUJITA, OSAMU, KUMIKO HIRAISHI, YOSHITO SUGINOBU, MASAYASU TAKEUCHI, and ISAMU NARABAYASHI. "Fundamental Studies of Oral Contrast Agents for MR : Comparison of Manganese Agent and Iron Agent." Japanese Journal of Radiological Technology 52, no. 12 (1996): 1613–18. http://dx.doi.org/10.6009/jjrt.kj00003109484.

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Nababan, Maxtulus Junedy, Herman Mawengkang, Tulus Tulus, and Sutarman Sutarman. "Hidden Markov Model to Optimize Coordination Relationship for Learning Behaviour." International Journal of Religion 5, no. 9 (May 27, 2024): 459–69. http://dx.doi.org/10.61707/52exbt60.

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School communities interact dynamically, much like the agents in a multi-agent system. For coordinated action, relationships between agents in a multi-agent system must be handled. One technique for persuading individuals to behave in a coordinated manner is to manage the role of agents in generating knowledge, attitudes, and practices. Managing these connections is difficult due to the large number of unknowns. Modeling can aid in the clarification of agent relationships. Coordination mechanisms can be modeled using Markov models. Agents can demonstrate and consider how their actions affect other agents in order to achieve desired behavior goals. This paper extends the state space of Partially Observable Markov Decision Processes (POMDPs) with an agent model to make them multi-agent friendly.
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Yunchyk, Valentyna, Natalia Kunanets, Volodymyr Pasichnyk, and Anatolii Fedoniuk. "Analysis of Artificial Intellectual Agents for E-Learning Systems." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 10 (December 2021): 41–57. http://dx.doi.org/10.23939/sisn2021.10.041.

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The key terms and basic concepts of the agent are analyzed. The structured general classification of agents according to the representation of the model of the external environment, by the type of processing information and by the functions performed is given. The classification of artificial agents (intellectual, reflex, impulsive, trophic) also is s analyzed. The necessary conditions for the implementation of a certain behavior by the agent are given, as well as the scheme of functioning of the intelligent agent. The levels of knowledge that play a key role in the architecture of the agent are indicated. The functional diagram of a learning agent that works relatively independently, demonstrating flexible behavior. It is discussed that the functional scheme of the reactive agent determines the dependence on the environment. The properties of the intelligent agent are described in detail and the block diagram is indicated. Various variants of agent architectures, in particular neural network agent architectures, are considered. The organization of level interaction in the multilevel agent architecture is proposed. Considerable attention is paid to the Will-architecture and InteRRaP- architecture of agents. A multilevel architecture for an autonomous agent of a Turing machine is considered.
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Zhang, Li, Zhi Qi, Hao Cui, Sen Hua Wang, Ya Hui Ning, and Qian Zhu Wang. "Exploring Agent-Based Modeling for Emergency Logistics Collaborative Decision Making." Advanced Materials Research 710 (June 2013): 781–85. http://dx.doi.org/10.4028/www.scientific.net/amr.710.781.

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Aiming at the requirements of urgency and dynamics in emergency logistics, this paper presents a multi-agent system (MAS) concept model for emergency logistics collaborative decision making. The suggested model includes three kinds of agents, i.e., role agent, function agent and assistant agent. Role agent excutes emergency logistics activities, function agent achieves the task requirements in every work phase and assistant agent helps organizing and visiting data. Two levels agent views serve as the basic skeleton of the MAS. Top level is the global decision-making view, which describes the task distribution process with multiple agents. Local level is the execution planning view, which simulates task executing process of the performer. Finally, an extended BDI agent structure model is proposed to help the implementation at application level.
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Botchkaryov, Alexey. "Multi-agent Coordination with Deferred Asynchronous Messaging in a Distributed Coordination Space." Advances in Cyber-Physical Systems 7, no. 2 (December 16, 2022): 83–90. http://dx.doi.org/10.23939/acps2022.02.083.

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A method of multi-agent coordination with deferred asynchronous messaging in a distributed coordination space has been proposed. The method has been based on the concept of multi-agent conditional interaction. The method has used 1) a distributed coordination space in which agents move, 2) the rules of state transitions for the coordination space nodes depending on the movements of agents, 3) the rules of agents move and state transitions depending on the states of the coordination space nodes, 4) a multi-agent coordination game based on the coordination space and the rules. The coordination space has been implemented based on the distributed shared memory of agents. The rules have been applied by exchanging deferred asynchronous messages between agents through the distributed shared memory. The agent's decisions about movement in the coordination space and their consequences are interpreted according to the rules in asynchronous messages. Delivery of messages to other agents has been deferred until these agents visit the corresponding nodes of the coordination space. This has ensured 1) mutual exclusion when agents choose conflicting actions, and 2) resilience of multi-agent coordination to agent failures and loss of coordinating messages. Four multi-agent coordination games have been considered as examples. The issue of fault tolerance of the proposed coordination method has been considered. The simulation results show that the use of the method ensures the resilience of multi-agent coordination to agent failures in the considered coordination games.
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Li, Xilun, Zhan Li, Xiaolong Zheng , Xuebo Yang , and Xinghu Yu . "The Study of Crash-Tolerant, Multi-Agent Offensive and Defensive Games Using Deep Reinforcement Learning." Electronics 12, no. 2 (January 8, 2023): 327. http://dx.doi.org/10.3390/electronics12020327.

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In the multi-agent offensive and defensive game (ODG), each agent achieves its goal by cooperating or competing with other agents. The multi-agent deep reinforcement learning (MADRL) method is applied in similar scenarios to help agents make decisions. In various situations, the agents of both sides may crash due to collisions. However, the existing algorithms cannot deal with the situation where the number of agents reduces. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we study a method to deal with a reduction in the number of agents in the training process without changing the structure of the neural network (NN), which is called the frozen agent method for the MADDPG (FA-MADDPG) algorithm. In addition, we design a distance–collision reward function to help agents learn strategies better. Through the experiments in four scenarios with different numbers of agents, it is verified that the algorithm we proposed can not only successfully deal with the problem of agent number reduction in the training stage but also show better performance and higher efficiency than the MADDPG algorithm in simulation.
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Tang, Jin Peng, Ling Lin Li, Shu Kun Liu, De Peng Hu, Me Ling Cai, and Xiao Qiu Liu. "Research of Intersection Navigation Algorithm Based on Intelligent Agent." Applied Mechanics and Materials 641-642 (September 2014): 690–93. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.690.

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In order to improve the vehicle alone path finding way, put forward a kind of road navigation algorithm based on agent. First put forward the intelligent design method, and then the intelligent design is mapped to different kinds of the intelligent agents. And then expounds the function of operation control agent, agent, Lane agent and intersection agent needs to realize. Finally, based on these agents of the navigation algorithm is designed using Dijkstra algorithm.
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Jian, Yu Mei, and Ming Chen. "A Multi-Agent Cooperation System Structure for GWSN." Applied Mechanics and Materials 321-324 (June 2013): 523–27. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.523.

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According to the characteristics of GWSN, a Multi-Agent cooperation system structure of GWSN is put forward based on the complexity and changeableness of water area and biologic growth. In this structure, the Agent is divided into mobile Agent and static Agent. Static Agent is responsible for collecting data, and mobile Agent is responsible for move between clusters. Each cluster cooperation agents are responsible for data transmission and the data fusion process is divided into four levels. It manages greenhouse wireless measurement and control system by distributed agents’ information timely interaction and cooperation.
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Sklar, Elizabeth, and Debbie Richards. "Agent-based systems for human learners." Knowledge Engineering Review 25, no. 2 (June 2010): 111–35. http://dx.doi.org/10.1017/s0269888910000044.

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AbstractApplying intelligent agent technologies to support human learning activities has been the subject of recent work that reaches across computer science and education disciplines. This article discusses agent-based approaches that have been designed to address a range of pedagogical and/or curricular tasks. Three types of agents are identified in the literature:pedagogical agents,peer-learning agents, anddemonstrating agents. Features of each type are considered, as well as the systems in which these agents are incorporated, examining common and divergent goals, system and agent architectures, and evaluation methodologies. Open issues are highlighted, and future directions for this burgeoning interdisciplinary field are suggested.
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Hassan, Razouki, and Hair Abdellatif. "Security for Mobile Agents: Trust Estimate for Platforms." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (August 1, 2015): 381. http://dx.doi.org/10.11591/tijee.v15i2.1553.

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The mobile agent has been seen as a promising distributed computing technology. The mobility characteristic of mobile agent makes it to travel often in open network. In this scenario, it is obvious that the mobile agents are vulnerable to various security threats. Protecting free-roaming mobile agents from malicious host and from other mobile agents has drawn much attention in recent years. The protection of mobile agents is considered as one of the greatest challenges of security, because the platform of execution has access to all the components of the mobile agent. In this paper, we present a new architecture paradigm of mobile agents, which allows the separation of the implementation tasks of the agent and its security mechanisms. Our approach is based on using two strategies of adaptation to adapt the mobile agent security at runtime, depending on the sensitivity of the services required to perform the duties of the agent and the degree of confidence of the visited platforms.
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