906 research outputs found

    Design and anticipation: towards an organisational view of design systems

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    Online Ensemble Learning of Sensorimotor Contingencies

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    Forward models play a key role in cognitive agents by providing predictions of the sensory consequences of motor commands, also known as sensorimotor contingencies (SMCs). In continuously evolving environments, the ability to anticipate is fundamental in distinguishing cognitive from reactive agents, and it is particularly relevant for autonomous robots, that must be able to adapt their models in an online manner. Online learning skills, high accuracy of the forward models and multiple-step-ahead predictions are needed to enhance the robots’ anticipation capabilities. We propose an online heterogeneous ensemble learning method for building accurate forward models of SMCs relating motor commands to effects in robots’ sensorimotor system, in particular considering proprioception and vision. Our method achieves up to 98% higher accuracy both in short and long term predictions, compared to single predictors and other online and offline homogeneous ensembles. This method is validated on two different humanoid robots, namely the iCub and the Baxter

    Frontoparietal action-oriented codes support novel task set implementation

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    A key aspect of human cognitive flexibility concerns the ability to rapidly convert complex symbolic instructions into novel behaviors. Previous research proposes that this fast configuration is supported by two differentiated neurocognitive states, namely, an initial declarative maintenance of task knowledge, and a progressive transformation into a pragmatic, action-oriented state necessary for optimal task execution. Furthermore, current models predict a crucial role of frontal and parietal brain regions in this transformation. However, direct evidence for such frontoparietal formatting of novel task representations is still lacking. Here, we report the results of an fMRI experiment in which participants had to execute novel instructed stimulus-response associations. We then used a multivariate pattern-tracking procedure to quantify the degree of neural activation of instructions in declarative and procedural representational formats. This analysis revealed, for the first time, format-unique representations of relevant task sets in frontoparietal areas, prior to execution. Critically, the degree of procedural (but not declarative) activation predicted subsequent behavioral performance. Our results shed light on current debates on the architecture of cognitive control and working memory systems, suggesting a contribution of frontoparietal regions to output gating mechanisms that drive behavior

    A Machine Learning and Deep Learning Framework for Binary, Ternary, and Multiclass Emotion Classification of Covid-19 Vaccine-Related Tweets

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    My research mines public emotion toward the Covid-19 vaccine based on Twitter data collected over the past 6-12 months. This project is centered around building and developing machine learning and deep learning models to perform natural language processing of short-form text, which in our case tweets. These tweets are all vaccine-related tweets and the goal of the classification task is for our models to accurately classify a tweet into one of four emotion groups: Apprehension/Anticipation, Sadness/Anger/Frustration, Joy/Humor/Sarcasm, and Gratitude/Relief. Given this data and the goal of the paper, we aim to answer the following questions: (1) Can a framework be developed for machine learning and deep learning multiclass classification models to accurately infer one of four listed emotion groups represented by a vaccine-related tweet? A follow-up to this question is: Can we improve the overall model performance by clustering the emotions into a ternary classification problem? (2) Is there a significant binary distinction that can be made between tweets that express “negative” emotions (Apprehension, Anticipation, Sadness, Anger, and Frustration) and “positive” emotions (Joy, Humor, Sarcasm, Gratitude, and Relief)? This research will present a framework that takes in the raw tweet data and through a pipeline that applies data preprocessing, feature extraction, data splitting & sampling, and ultimately emotion classification. Through these questions, the aim is not only to determine the overall acceptance and sentiment of the vaccines by the public but also to understand the steps public health officials can take to further educate hesitant and/or fearful citizens while also incentivizing it

    Annotated Bibliography: Anticipation

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    Graph dynamics : learning and representation

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    Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 58-60).Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a framework for representation, graphs invite us to simplify and view the world as objects of pure structure whose properties are fixed in time, while the phenomena they are supposed to model are actually often changing. A node alone cannot represent a table leg, for example, because a table leg is not one structure (it can have many different shapes, colors, or it can be seen in many different settings, lighting conditions, etc.) Theories of knowledge representation have in general concentrated on the stability of symbols - on the fact that people often use properties that remain unchanged across different contexts to represent an object (in vision, these properties are called invariants). However, on closer inspection, objects are variable as well as stable. How are we to understand such problems? How is that assembling a large collection of changing components into a system results in something that is an altogether stable collection of parts?(cont.) The work here presents one approach that we came to encompass by the phrase "graph dynamics". Roughly speaking, dynamical systems are systems with states that evolve over time according to some lawful "motion". In graph dynamics, states are graphical structures, corresponding to different hypothesis for representation, and motion is the correction or repair of an antecedent structure. The adapted structure is an end product on a path of test and repair. In this way, a graph is not an exact record of the environment but a malleable construct that is gradually tightened to fit the form it is to reproduce. In particular, we explore the concept of attractors for the graph dynamical system. In dynamical systems theory, attractor states are states into which the system settles with the passage of time, and in graph dynamics they correspond to graphical states with many repairs (states that can cope with many different contingencies). In parallel with introducing the basic mathematical framework for graph dynamics, we define a game for its control, its attractor states and a method to find the attractors. From these insights, we work out two new algorithms, one for Bayesian network discovery and one for active learning, which in combination we use to undertake the object recognition problem in computer vision. To conclude, we report competitive results in standard and custom-made object recognition datasets.by Andre Figueiredo Ribeiro.S.M
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