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16 results for “multi-agent system”
The Missing Link: A New Skeleton for Evolutionary Multi-Agent Systems in Erlang
<p>Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton for parallel EMAS computations, written in Erlang. The skeleton enables us to capture a wide variety of concrete evolu- tionary computations that can exploit the same underlying parallel implemen- tation. We demonstrate the use of our skeleton on two different evolutionary computing applications: i) computing the minimum of the Rastrigin function; and ii) solving an urban traffic optimisation problem. We show that we can ob- tain very good speedups (up to 142.44× the sequential performance using 244 threads on a 61-core accelerator) on a variety of different parallel hardware, while requiring very little parallelisation effort.</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
BRAIN Journal - Lamport's algorithm - Figure 2 from paper "Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time"
<p>Figure 2. Lamport’s algorithm</p> <p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took place. This relation can be directly observed in two situations (figure 2): 1. If two events happen during the same process, the order of the happening is indicated by the common clock; 2. When two processes communicate through a message, the event that corresponds to sending the precise message always happens before the event of receiving it (i.e. the message). If two events, 1 a and 2 a , are produced in different processes that do not exchange messages (neither directly nor indirectly), then it is not certain if 1 2 a → a or 2 1 a → a . In this case it is said that these events are competitive, which means that it is not known which one happened first (and it is not a must-know thing either).</p>
Figure 3. The multi-agent system-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>The authority is given to a single agent<br> that is symbolically situated on the top level; this agent would be considered the root of an<br> arborescent structure; but the stages of the process are provided by groups of agents, respectively by<br> the relations of communication between them.<br> Thus, we would deal with a ZERO AGENT and several groups of agents. Such a group is<br> made up of several agents, each and one of these agents accomplishing a certain role in the process<br> of synchronization (figure 3).</p>
Figure 2. Lamport's algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>In order to synchronize logical clocks, Lamport [3] defined the relationship “happened<br> before” (preceded) which implies that the expression 1 2 a → a means “ 1 a occurred before 2 a ”, and it<br> means that all the processes coincide in the fact that 1 a took place first, and subsequently 2 a took<br> place. This relation can be directly observed in two situations (figure 2):<br> 1. If two events happen during the same process, the order of the happening is indicated by<br> the common clock;<br> 2. When two processes communicate through a message, the event that corresponds to<br> sending the precise message always happens before the event of receiving it (i.e. the<br> message).</p>
Figure 1. Cristian's Algorithm-Optimization of Distributed Systems Using Multi-Agent Systems with Virtual Time
<p>Cristian’s Algorithm (figure 1) is a method for clock synchronization which can be used in<br> many fields of distributive computer science. It suffers, though, in implementations using a single<br> server, making it unsuitable for many distributive applications where redundancy may be crucial.</p>
Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The negotiations between agents are subject to optimization based on “knowledge” that is<br> derived from complete production models, yield models or even sparse models as expressed in<br> fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models<br> provide additional information useful to the design of a successful strategy for optimal management<br> [15] (illustrated in Figure 5).</p>
Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p> The value of the final product incorporates not only quantity issues but also<br> quality issues, which are difficult to be measured or even estimated. The environment of each agent<br> is defined by the same parameters that define the physical environment in addition to internal states<br> reported by each agent.</p>
Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"
<div> <div>Dataset accompanying the paper "<em>Adaptive connectivity control in networked multi-agent systems: A distributed approach</em>" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> <p>Detailed information and instructions are available in the README file within the dataset.</p> </div> </div> </div>
Graph Laplacians used in the article "Numerically Efficient $H_{\infty}$ Analysis of Cooperative Multi-Agent Systems"
<p>The repository contains four graph Laplacians written in the Matlab format. These matrices are used in the section "Numerical Examples" in the article "Numerically Efficient $H_{\infty}$ Analysis of Cooperative Multi-Agent Systems".</p>
CodePori: Large Scale System for Autonomous Software Development by Using Multi-Agents
<p>This dataset accompanies the paper <strong>"CodePori: A Large-Scale System for Autonomous Software Development Using Multi-Agents."</strong> The dataset is recorded in an MS Excel file, which contains the following sheets, with a brief description of each provided below:</p> <ol> <li> <p><strong>Selected Projects:</strong> Contains the descriptions of the 20 selected projects along with the GitHub URL for each.</p> </li> <li> <p><strong>Modifications:</strong> Contains details of the modifications made to the projects to ensure successful execution.</p> </li> <li> <p><strong>Outputs:</strong> Contains the output for each project.</p> </li> <li> <p><strong>Failed Projects:</strong> Contains data on the projects that failed.</p> </li> </ol>
Leveraging Multi-Agent Systems and Decentralised Autonomous Organisations for Tax Credit Tracking: A Case Study of the Superbonus 110% in Italy
<p>The present dataset was obtained as output of the simulation of Secure Fiscal Credit Model (SCFM) demonstrator. The software, inspectable at the link in the repository url, allows to simulate the workflow and payments involved in the execution of Italian Superbonus 110% fiscal credits model. Two different Scenarios were tested, parameters are reported in the following table.</p> <table> <tbody> <tr> <td> </td> <td><strong>GC payment</strong></td> <td><strong>Tech Asseveration</strong></td> <td><strong>Open</strong></td> <td><strong>Anticipation</strong></td> <td><strong>SAL1</strong></td> <td><strong>SAL2</strong></td> <td><strong>EOW</strong></td> <td><strong>Tech fee</strong></td> </tr> <tr> <td><strong>Scenario 1</strong></td> <td>50%</td> <td>50%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> <tr> <td><strong>Scenario 2</strong></td> <td>25%</td> <td>25%</td> <td>10%</td> <td>10%</td> <td>30%</td> <td>40%</td> <td>10%</td> <td>15%</td> </tr> </tbody> </table> <p>File named with S1 prefix refer to Scenario 1, while those with S2 prefix refer to Scenario 2.</p>
Neural-logic Multi-agent System for flood event detection
<p>This repository contains the source code for the paper "Neural-logic Multi-agent System for flood event detection". The code implements a multi-agent system (MAS) designed to detect flooding events.</p>
Patient Centered Multi-Agent Decision Support System (PCM-DSS) for Healthcare Providers
ClinicalTrials.gov study NCT07063420. IPD Sharing: YES. Countries: 1. Publications: 0.
Optimal Standard Treatment Selection for Solid Tumor Patients by Biologically-informed Multi-agent System
ClinicalTrials.gov study NCT06824792. IPD Sharing: NO. Countries: 1. Publications: 0.
Experiments for "Time-varying formation tracking for second order multi-agent systems: an experimental approach for wheeled robots"
<p>In this paper, a time-varying formation tracking protocol for second order Multi-Agent 1<br> Systems (MASs) is presented. The time-varying formation considers translation, rotation and scaling 2<br> of the geometric pattern that defines the formation. The control law is simple yet effective, and is 3<br> composed of a trajectory tracking control and a consensus control that considers position and velocity 4<br> feedback of the connected agents in the MAS. The closed-loop system is asymptotically stable, and 5<br> this is proved using the Gershgoring’s disk theorem. The performance of the protocol is extensively 6<br> tested in experiments using a dynamic extension of the differential-drive robot model. The protocol 7<br> is tested for different communication topologies and also deals with switching topologies. The 8<br> proposed protocol presents good performance to both time-varying formation and topology changes. 9<br> Moreover, a comparison with an existing controller and with only trajectory tracking control is 10<br> provided, showing that the proposed protocol preserves in a better way the formation for all the 11<br> tested topologies.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.