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506 results for “decision-making”
Figure 2 in Getting to a decision: using structured decision-making to gain consensus on approaches to invasive species control
Figure 2. Calculated utility of avoiding fish species at-risk and not at-risk of extirpation in Cultus Lake, BC, across all combinations of smallmouth bass control. Utility is presented on a scale from 0–1.
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>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 17. Autonomous Decision-Making Architecture
<p>An overview of the decision-making architecture is presented in Figure 17. The architecture was<br> guided by two core concepts. The first core concept is that human intelligence bases on a<br> combination of low-level and high-level mechanisms. Low-level mechanisms are mainly<br> predefined. They are not in all situations completely accurate but have the advantage of being fast.<br> High-level mechanisms are not predefined and thus slower but more accurate. The second core<br> concept concerns the use of so-called emotions as mechanism for the evaluation of information</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 18. Different Modules involved in the Autonomous Decision-Making Process
<p>The basic functioning of this architecture is now described in the following step by step<br> using Figure 18a-f, where always the relevant modules of the model are highlighted for better<br> comprehension.</p>
Photomorphogenesis for Robot Self-assembly: Adaptivity, Collective Decision-making, and Self-repair
<p>Self-assembly in biological systems is an inspiration for engineered large-scale multi-modular systems with desirable characteristics, such as robustness, scalability, and adaptivity. Previous works have shown that simple mobile robots can be used to emulate and study self-assembly behaviors. However, many of these studies were restricted to rather static and inflexible aggregations in predefined shapes, and were limited in adaptivity compared to that observed in nature. We propose a photomorphogenesis approach for robots using our vascular morphogenesis model---a light-stimuli directed method for multi-robot self-assembly inspired by the tissue growth of trees. Robots in the role of `leaves' collect a virtual resource that is proportional to a real, sensed environmental feature. This resource is then shared throughout the whole robot aggregate and determines where it grows or shrinks as a reaction to the dynamic environment. In our approach the robots use supplemental bioinspired models to collectively select a seed robot to decide who starts to self-assemble (and where), or to assemble static aggregations. The robots then use our vascular morphogenesis model to aggregate in a directed way preferring bright areas, hence resembling natural phototropism (growth towards light). In this assembly, they are adaptive and able to react to a dynamic environment by collectively and autonomously rearranging the aggregate, discarding outdated parts and growing new ones. In representative experiments, the self-assembling robots collectively make rational decisions on where to grow. Cutting off parts of the aggregate triggers a self-organizing repair process in the robots, and the parts regrow. All these capabilities of adaptivity, collective decision-making, and self-repair in our robot self-assembly originate directly from self-organized behavior of the vascular morphogenesis model. Our approach opens up opportunities for self-assembly with reconfiguration on short time-scales with high adaptivity of dynamic forms and structures.</p>
Fig. 2 in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 2. Decision time (s) spent by the A. subterraneus target worker according to number of trips (n) made by the respective target worker and the concentration of pheromone manipulated on the branch that does not lead to the food, estimated by total flow of foragers.
Fig. 1. Y in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 1. Y-trail system with branches of equal length (225 mm). Branches arranged at an angle of 60◦ connected to a bifurcation. Decision lines (LD) established at fixed points 140 mm far from the bifurcation center on the right and left branches, and 25 mm from the base branch to calculate the A. subterraneus workers' frequency of passage when half of their bodies had crossed each LD.
Data from: Distinct developmental trajectories for risky and impulsive decision-making in chimpanzees
<p>Human adolescence is characterized by a suite of changes in decision-making and emotional regulation that promote risky and impulsive behavior. Accumulating evidence suggests that behavioral and physiological shifts seen in human adolescence are shared by some primates, yet it is unclear if the same cognitive mechanisms are recruited. We examined developmental changes in risky choice, inter-temporal choice, and emotional responses to decision outcomes in chimpanzees, our closest-living relatives. We found that adolescent chimpanzees were more risk-seeking than adults, as in humans. However, chimpanzees showed no developmental change in inter-temporal choice, unlike humans, although younger chimpanzees did exhibit elevated emotional reactivity to waiting compared to adults. Comparisons of cortisol and testosterone indicated robust age-related variation in these biomarkers, and patterns of individual differences in choices, emotional reactivity, and hormones also supported a developmental dissociation between risk and choice impulsivity. These results show that some but not all core features of human adolescent decision-making are shared with chimpanzees.</p>
Intraparietal stimulation disrupts negative distractor effects in human multi-alternative decision-making
<p>There has been debate about whether addition of an irrelevant distractor option to an otherwise binary decision influences which of the two choices is taken. We show that disparate views on this question are reconciled if distractors exert two opposing but not mutually exclusive effects. Each effect predominates in a different part of decision space: 1) a positive distractor effect predicts high-value distractors improve decision-making; 2) a negative distractor effect, of the type associated with divisive normalisation models, entails decreased accuracy with increased distractor values. Here, we demonstrate both distractor effects coexist in human decision making but in different parts of a decision space defined by the choice values. We show disruption of the medial intraparietal area (MIP) by transcranial magnetic stimulation (TMS) increases positive distractor effects at the expense of negative distractor effects. Furthermore, individuals with larger MIP volumes are also less susceptible to the disruption induced by TMS. These findings also demonstrate a causal link between MIP and the impact of distractors on decision-making via divisive normalisation.</p>
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Size-selective harvesting impacts learning and decision-making in zebrafish
<p><span>Size-selective harvesting common to fisheries is known to evolutionarily alter life-history and behavioural traits in exploited fish populations. Changes in these traits may in turn modify learning and decision-making abilities through energetic trade-offs with brain investment that can vary across development or via correlations with personality traits. We examined the hypothesis of size-selection-induced alteration of learning performance in three selection lines of zebrafish (<em>Danio</em> <em>rerio</em>) generated through intensive harvesting for large, small and random body-size for five generations followed by no further selection for ten generations that allowed examining evolutionarily fixed outcomes. We tested associative learning ability throughout ontogeny in fish groups using a colour-discrimination paradigm with a food reward, and the propensity to make group decisions in an associative task. All selection lines showed significant associative abilities that improved across ontogeny. The large-harvested line fish showed a significantly slower associative learning speed as subadults and adults than the controls. We found no evidence of memory decay as a function of size-selection. Decision-making speed did not vary across lines, but the large-harvested line made faster decisions during the probe trial. Collectively, our results show that size-selective harvesting evolutionarily alters associative and decision-making abilities in zebrafish, which could affect resource acquisition and survival in exploited fish populations. </span></p>
Meta-analysis on Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making
<p>This dataset corresponds to a meta-analysis to examine the peer-reviewed scholarship on stakeholder engagement in co-production processes in environmental decision-making.</p> <p>The dataset was developed by searching keywords, abstracts, and titles in Scopus on three key themes: co-production, transdisciplinary, and community-based participatory research. A total of 709 publications were reviewed, from which 144 met the selection criterion. The 144 articles constitute this dataset. They pertain to articles published between January 2005 and June 2020.</p> <p>The data includes information on the following topics:</p> <ol> <li>Number of unique case studies reported</li> <li>The projects’ time span</li> <li>When stakeholder engagement activities occur during the project cycle</li> <li>Are stakeholders (non-university researchers) an author</li> <li>Geographic region of project</li> <li>Environmental issues addressed</li> <li>Geographic scale of project </li> <li>Types of stakeholders engaged </li> <li>How stakeholders were engaged</li> <li>What output resulted from stakeholder engagement</li> <li>Approach to stakeholder engagement</li> <li>Factors that enabled stakeholder engagement</li> <li>Barriers or challenges to engagement</li> <li>Societal impacts associated with stakeholder engagement</li> <li>Direct evidence of societal impacts of stakeholder engagement</li> </ol> <p>The methods and analysis are presented in "Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making," which was submitted for publication in 2022 to the Journal <em>World Development</em>. A draft of the manuscript can be provided upon request to authors.</p>
Dataset of TMLR 2024 Paper "Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games"
<p>This is a part of dataset of the paper published in TMLR 2024 (Transactions on Machine Learning Research, <a href="https://jmlr.org/tmlr/" target="_blank" rel="noopener">https://jmlr.org/tmlr/</a>).</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr" target="_blank" rel="noopener">https://github.com/DSobscure/cgi_drl_platform/tree/playstyle_similarity_tmlr</a></p> <p> </p>
Improving Providers' Decision-Making and Reducing Information Overload Using Information Visualization in EHRs
ClinicalTrials.gov study NCT05937646. IPD Sharing: YES. Countries: 1. Publications: 1.
Communication to Improve Shared Decision-Making in ADHD
ClinicalTrials.gov study NCT02716324. IPD Sharing: YES. Countries: 1. Publications: 6.
Decision-making in dynamic, continuously evolving environments: Quantifying the flexibility of human choice
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Data from: Quantity discrimination, decision-making, and the role of early-life conditions in a lizard
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Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
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Size-selective harvesting impacts learning and decision-making in zebrafish
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Data from: Frontal noradrenergic and cholinergic transients exhibit distinct spatiotemporal dynamics during competitive decision-making
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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.