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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
Inductive Reasoning00:59

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

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Related Experiment Videos

A Learning Algorithm for Multimodal Grammar Inference.

A D'Ulizia, F Ferri, P Grifoni

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |July 5, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new algorithm for automatically generating multimodal grammars, reducing development costs. The method effectively learns and refines grammars from examples, improving multimodal interface input processing.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Computational Linguistics

    Background:

    • Developing and maintaining multimodal grammars for interfaces is costly and complex.
    • Existing natural language processing algorithms for grammar inference need extension for multimodal applications.

    Purpose of the Study:

    • To investigate novel algorithmic solutions for automating multimodal grammar generation and updating.
    • To propose a new grammar inference mechanism for learning multimodal grammars from positive samples.

    Main Methods:

    • The proposed algorithm infers multimodal grammars from positive sentence samples.
    • It employs two learning operators and minimum description length metrics.
    • The process involves initial grammar generation followed by refinement to avoid over-generalization.

    Main Results:

    • The algorithm successfully learns multimodal grammars capable of parsing positive samples.
    • Experimental results demonstrate acceptable performance and a high probability of parsing valid sentences.
    • The method shows promise in improving grammar description and mitigating over-generalization.

    Conclusions:

    • The novel grammar inference mechanism offers an efficient solution for automated multimodal grammar generation.
    • This approach can significantly reduce the costs associated with multimodal interface development and maintenance.
    • The algorithm's effectiveness in learning and refining grammars is validated by experimental performance.