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7 results for “intelligent agent”
Figure 2. Screen capture of the current application-INTELLIGENT AGENT FOR ACQUISITION OF THE MOTHER TONGUE VOCABULARY
<p>From this screen capture (figure 2), you can observe that, for example, the noun car (masina)<br> is in a great correspondence with the correct word car(masina), and in the same correspondence<br> with the incorrect word small (mic). But this is due to the fact that the system worked only with few<br> examples. After a training with much more examples, the system will increase the correspondence<br> between the concept/object car and the word car, and the correspondence between the object car and<br> the word little will remain smallest.</p>
Figure 1. Associations between objects and words-Intelligent Agent for Acquisition of the Mother Tongue Vocabulary
<p>So, the learning process is very complex, and it is a probabilistic one. Thus, a second<br> example is required, and even more than that. Normally, the mother speaks naturally to her child,<br> she speaks with love and affection, she doesn’t “judge” or “program” what to say to her child.<br> Therefore, another day she will tell her child, for example, “My darling son, let’s drink the milk<br> from the cup.”</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 19. AnyLogic Implementation of Decision-Making Architechture in an Virtual Autonomous Agent Environment
<p>Figure 19 shows a screenshot of the test implementation. The picture in the middle shows the modules and interfaces<br> of the decision-making architecture which were realized by so-called “active objects” and “ports”.<br> On the left side, the implemented modules are listed. In the right lower corner of the figure, the<br> agents and the virtual environment are displayed. The environment comprises different “objects”<br> (food sources, obstacles, predators, other agents, etc.). In order to survive, the agents have to access<br> food sources. However, the accessing of food sources bears difficulties and risks.</p>
Video Figure: Intelligent Agents and Networked Buttons Improve Free-Improvised Ensemble Music-Making on Touch-Screens
<p>This video figure is an overview of our study comparing two designs for network communications between touch-screen musical instruments played in free-improvised ensemble performances.</p> <p>The video shows an overview of the touch-screen app (PhaseRings) used in the study and each of the interface conditions.</p> <p>The abstract of the paper relating to this figure is as follows:</p> <p>We present the results of two controlled studies of free-improvised ensemble music-making on touch-screens. In our system, updates to an interface of harmonically-selected pitches are broadcast to every touch-screen in response to either a performer pressing a GUI button, or to interventions from an intelligent agent. In our first study, analysis of survey results and performance data indicated significant effects of the button on performer preference, but of the agent on performance length. In the second follow-up study, a mixed-initiative interface, where the presence of the button was interlaced with agent interventions, was developed to leverage both approaches. Comparison of this mixed-initiative interface with the always-on button-plus-agent condition of the first study demonstrated significant preferences for the former. The different approaches were found to shape the creative interactions that take place. Overall, this research offers evidence that an intelligent agent and a networked GUI both improve aspects of improvised ensemble music-making.</p>
Multi-center Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
ClinicalTrials.gov study NCT05754476. IPD Sharing: YES. Countries: 1. Publications: 3.
Use of a Novel Artificial Intelligence Assisted Platform to Assess Optimal Dosing and Treatment Strategy of Erythropoiesis-stimulating Agents (ESA) in Hemodialysis Patients
ClinicalTrials.gov study NCT05032651. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
VALIDATE: Virtual Agent Linked Intelligent Disease Assessment Tool Engine
ClinicalTrials.gov study NCT03153618. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
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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.