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About 10,000 papers for “Reinforcement”

Reinforcement learning with limited reinforcement

Finale Doshi, Joëlle Pineau, Nicholas Roy · 2008

Cited by 24

Reinforcement

Encyclopedia of Educational Psychology · 2008

Cited by 0

Reinforcement

William M. Baum · The SAGE Encyclopedia of Theory in Psychology · 2016

Cited by 0

Reinforcement

Claire McCarthy, Douglas Eastman, David E. Garets · HIMSS Publishing eBooks · 2018

Cited by 0

Reinforcement

R. Dodge Woodson · Elsevier eBooks · 2011

Cited by 0

This chapter identifies reinforcement as a major aspect of the social skills model of human communication. Arising from the operant conditioning Stimulus-Response (S-R) theory introduced by B.F. Skinner, and developed over the past 50 years in a number of related areas of theory, research, and application, the chapter delineates the core dimensions of the field. The distinction between feedback and reinforcement is highlighted, and the importance of social reinforcers in human communication is underscored. Examples of more recent applications and developments, especially in the rapid technological revolution of the third decade of the 21st century, are included.

Cited by 1

Reinforcement

2021

Cited by 0

Reinforcement

Martín García, Estefanía · QRU Quaderns de Recerca en Urbanisme · 2024

Reparar es un acto propio del ser humano, Homo reparans (Spelman, 2002), enfocado a la recuperación funcional de una relación, sistema o mecanismo dañado. Su propósito consiste en devolver la resistencia, la estabilidad y el equilibrio al edificio maltrecho (Torres Balbás, 1933). Sin embargo, en patrimonio las únicas reparaciones plausibles son las de urgencia, por su carácter improvisado (reversible) y su cometido correctivo. Su permanencia efímera se limita a la ejecución del proyecto de restauración arquitectónica (con carga crítica e histórico-artística) cuyos pasos previos y protocolarios incluyen la reparación y la consolidación.

Cited by 0Open access

Reinforcement

Montrose M, Lauren B. Resnick · 2016

These 2 papers examine the application of reinforcement principles in natural social settings. The first paper concentrates on the methodology of applied research, using an experimental as opposed to an observational strategy. That is, the experimenter intervenes with special ntreatments &quot; in a real-life social institution, much as he would in a laboratory experiment, and the behaviors studied are typically of real social concern. The 2nd paper considers a number of specific questions raised by the attempt to apply reinforcement principles in educational and therapeutic settings and suggests some lines of further research. Several concerns are: (1) the question of whether and how behavior can be maintained after special programs of reinforcement are terminated; (2) extensive, systematic study of the relationship between nprogrammed &quot; and n natural&apos;, rewards is badly needed before reinforcement principles can be widely and well used in applied settings; and (3) developing effective training and maintenance programs for reinforcement agents. (Author/TA),tge c-xG-4 /-&quot;:4 rn

Cited by 0

Reinforcement

A. Mooman, O. Basir, A. Younes · 2012

Abstract � Search engines and information retrieval (IR) systems provide a mechanism for users to access large amounts of information available through the Internet. However, in order to find the desired information, the user has to go through a staggering amount of information retrieved from highly dynamic resources. Experimental results show that the approach proposed for constructing specialized domains improves the precision of information retrieval. Our approach involves enriching the user’s query with related linguistic semantic and statistical semantic related concept terms. We employ natural language process (NLP) techniques such as WordNet engine to enrich the user’s query with semantic lexical synonymous terms and a probabilistic topic model such as latent dirichet allocation (LDA) to extract highly ranked topic from a query’s retrieved information. Furthermore, an intelligent learning algorithm, reinforcement learning (RL) is integrated into the design to assist end users in selecting the concept domains that are most relevant to their needs. 1.

Cited by 0