scholarly journals Robots Learn Writing

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Huan Tan ◽  
Qian Du ◽  
Na Wu
Keyword(s):  
Humanoid Robot ◽  
Cognitive Architecture ◽  
Semantic Knowledge ◽  
Data Set ◽  
Learning Stage ◽  
Motion Trajectories ◽  
Knowledge Models ◽  
Cognitive Information Processing ◽  
Cognitive Information ◽  
Writing Movements

This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model. Using this algorithm, the knowledge models are established. Learned motion and knowledge models are stored in a 2D latent space. Gaussian Process (GP) method is used to model and represent these models. Practical experiments are carried out on a humanoid robot, named ISAC, to learn the semantic representations of numbers and the movements of writing numbers through imitation and to verify the effectiveness of this framework. This framework is applied into training a humanoid robot, named ISAC. At the learning stage, ISAC not only learns the dynamics of the movement required to write the numbers, but also learns the semantic meaning of the numbers which are related to the writing movements from the same data set. Given speech commands, ISAC recognizes the words and generated corresponding motion trajectories to write the numbers. This imitation learning method is implemented on a cognitive architecture to provide robust cognitive information processing.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Cognitive Information Processing Measures as Predictors of Monitoring Performance

1982 ◽  
Vol 54 (3_suppl) ◽  
pp. 1299-1302 ◽  
Author(s):  
Douglas Cellar ◽  
Gerald V. Barrett ◽  
Ralph Alexander ◽  
Dennis Doverspike ◽  
Jay C. Thomas ◽  
...  
Keyword(s):  
Information Processing ◽  
Visual Search ◽  
Short Term Memory ◽  
Female College Students ◽  
Short Term ◽  
Term Memory ◽  
Cognitive Information Processing ◽  
Precise Understanding ◽  
Cognitive Information ◽  
Complex Monitoring

To obtain a more precise understanding of the constructs underlying complex monitoring, measures of short-term memory and visual search were administered to 7 male and 13 female college students. The hypothesis was that more rapid short-term memory and visual search would be related to successful monitoring. A correlational analysis indicated that choice reaction time was related to performance ( r = –.38 and –.43) while rate of serial comparisons was not ( r = –.08 and –.28). It was concluded that information-processing measures enhanced the understanding of the underlying processes in monitoring beyond that provided by traditional cognitive tests.

scholarly journals Validated measures of semantic knowledge and semantic control: Normative data from young and older adults for more than 300 semantic judgements

2021 ◽  
Author(s):  
Wei Wu ◽  
Paul Hoffman
Keyword(s):  
Normative Data ◽  
Semantic Processing ◽  
Feature Matching ◽  
Cognitive Architecture ◽  
Later Life ◽  
Semantic Knowledge ◽  
Matching Task ◽  
Large Set ◽  
Semantic Cognition ◽  
Control Processes

Recent studies suggest that knowledge representations and control processes are the two key components underpinning semantic cognition, and are also crucial indicators of the shifting cognitive architecture of semantics in later life. Although there are many standardized assessments that provide measures of the quantity of semantic knowledge participants possess, normative data for tasks that probe semantic control processes are not yet available. Here, we present normative data from more than 200 young and older participants on a large set of stimuli in two semantic tasks, which probe controlled semantic processing (feature-matching task) and semantic knowledge (synonym judgement task). We verify the validity of our norms by replicating established age- and psycholinguistic-property-related effects on semantic cognition. Specifically, we find that older people have more detailed semantic knowledge than young people but have less effective semantic control processes. We also obtain expected effects of word frequency and inter-item competition on performance. Parametrically varied difficulty levels are defined for half of the stimuli based on participants’ behavioral performance, allowing future studies to produce customized sets of experimental stimuli based on our norms. We provide all stimuli, data and code used for analysis, in the hope that they are useful to other researchers.

Lemons problem in collaborative consumption platforms

2018 ◽  
Vol 28 (3) ◽  
pp. 746-766 ◽  
Author(s):  
Joonheui Bae ◽  
Dong-Mo Koo
Keyword(s):  
Information Processing ◽  
Information Search ◽  
Intention To Use ◽  
Collaborative Consumption ◽  
Content Type ◽  
Processing Styles ◽  
Cognitive Information Processing ◽  
Other Information ◽  
Cognitive Information ◽  
Textual Cues

Purpose Most of the research on collaborative consumption platforms (CCPs) has focused on motivational drives, and little research has been conducted on the problem of unbalanced information sharing, also known as the “lemons problem,” and signals. The paper aims to discuss these issues. Design/methodology/approach This study conducted a netnography and an experiment. Findings The netnographic study showed that participants tend to use low ratings and negative reviews as cues implying more searches, use ratings as an anchor to adjust other information, and employ differing cognitive information-processing styles. The experimental results show that, in a normal environment (when ratings are high), visualizers (verbalizers) have more of an intention to use CCPs when they are exposed to abundant pictures (textual cues); however, when the cues lead to a further information search (when the ratings are low), this search behavior pattern is reversed: visualizers (verbalizers) have more of an intention to use CCPs when they are exposed to abundant textual cues (pictures). Research limitations/implications This study extends previous research by showing that people frequently use differing heuristics depending on the context; that ratings have an anchoring effect and guide people in selecting a signal to use and condition how they use it; and that visualizers prefer text cues to pictorial cues when trying to make informed decisions under a condition that points to a further information search. These results are opposite of previous assertion. Practical implications Marketers are advised to provide a mechanism by which users can extract the cues they need and reduce the less urgent ones; devise a mechanism that screens participants and divides them into two categories: those who post honest evaluations and those who do not; and reduce the opportunistic behaviors of partners on both sides. Originality/value The current study addresses consumers’ use of information posted by other consumers on CCPs and demonstrates that participants use low ratings and negative reviews as cues implying more searches, use ratings as an anchor to adjust other information, and employ differing cognitive information-processing styles. Previous research rarely addressed these information search behaviors of consumers on CCPs.

Modulation Scheme for Biasing the Emotional Process of Autonomous Agents

2020 ◽  
pp. 339-361
Author(s):  
Sergio Castellanos ◽  
Luis-Felipe Rodríguez ◽  
J. Octavio Gutierrez-Garcia
Keyword(s):  
Computational Models ◽  
Autonomous Agents ◽  
Cognitive Architecture ◽  
Modulation Scheme ◽  
Membership Functions ◽  
Real World Data ◽  
Emotional Process ◽  
Fuzzy Membership Functions ◽  
Cognitive Components ◽  
Cognitive Information

Autonomous agents (AAs) are capable of evaluating their environment from an emotional perspective by implementing computational models of emotions (CMEs) in their architecture. A major challenge for CMEs is to integrate the cognitive information projected from the components included in the AA's architecture. In this chapter, a scheme for modulating emotional stimuli using appraisal dimensions is proposed. In particular, the proposed scheme models the influence of cognition on appraisal dimensions by modifying the limits of fuzzy membership functions associated with each dimension. The computational scheme is designed to facilitate, through input and output interfaces, the development of CMEs capable of interacting with cognitive components implemented in a given cognitive architecture of AAs. A proof of concept based on real-world data to provide empirical evidence that indicates that the proposed mechanism can properly modulate the emotional process is carried out.

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