Sentence examples for learning automatic from inspiring English sources

controlled performance based on formal rule learning; automatic performance in a test situation; controlled performance based on implicit (unconscious) learning.

The goal of this paper is to present Computer Go by showing the links between existing studies on Computer Go and different AI related domains: evaluation function, heuristic search, machine learning, automatic knowledge generation, mathematical morphology and cognitive science.

The novel learning algorithm proposed has the main features of competitive learning, automatic generation of the network structure and binary representation of the connection strengths (weights).

In order to give impetus to the multidomain learning ability of representation learning, automatic representation learning [28], biased representation learning [26], cross-domain representation learning [27], and some other related techniques [29] have been proposed in recent years.

Some fields of application of AI are automatic problem solving methods for knowledge representation and knowledge engineering, machine vision and pattern recognition, artificial learning, automatic programming, the theory of games, and so forth.

The criteria for learning vs. automatic came from (1) the jerkiness vs. smoothness of the new speed profiles along the curved hand paths, and (2) the ease with which the animals switched from one experimental block to the next.

Let us visualize the various approaches in Fig. 1: Scenario A shows typical unsupervised ML: the algorithm is applied on the raw data and learns fully automatic, because it does not require a human to manually label the data.

Habitual smoking is a form of psychological addiction, which may coexist with chemical dependence, where there is no true reason for smoking except that it has become learned and automatic, with no influence on the smoker's mood or affect[ 14].

Another technique employs "automatic" learning to classify sensor inputs for instance, into objects or situations or to translate sensor states directly into desired behaviour.

It allows, without changing of algorithm, to carry out classifications using a supervised (directed) or unsupervised (automatic) learning stage.

Owing to the success of deep learning techniques in automatic speech recognition, deep neural networks (DNNs) have been used as acoustic models for statistical parametric speech synthesis (SPSS).