Dev E-R: A computational model of early cognitive development as a creative process

Aguilar, Wendy; Pérez, Rafael Pérez y

doi:10.1016/j.cogsys.2014.09.002

Cited by 16 publications

(18 citation statements)

References 21 publications

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“…Pérez y Pérez and Sharples [34] turned a cognitive model of the writer into an algorithm for narrative generation that cycles through modes of reflection and generation, while Pérez y Pérez [35] outlines a related model for the collaborative generation of stories. Aguilar and Pérez y Pérez [1] abstract this model of reflection and generation to view our early cognitive development as an inherently creative process. Veale and Valitutti [51] generate comic narratives by forcing familiar but ill-paired characters into familiar plot forms, while Victor Raskin, one of the first to bring AI methods to bear on narrative jokes, collects much relevant research in Raskin [40].…”

Section: In Thought and Actionmentioning

confidence: 99%

Leaps and Bounds: An Introduction to the Field of Computational Creativity

Veale

Pérez

2020

New Gener. Comput.

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Section: In Thought and Actionmentioning

confidence: 99%

Leaps and Bounds: An Introduction to the Field of Computational Creativity

Veale

Pérez

2020

New Gener. Comput.

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“…For example, Li and Meng [15] used SVM to learn sample and symbol representation of objects. Developmental Engagement-Reflection (Dev E-R) model [16] simulates the assimilation-accommodation adaptation process. Ramik et al [17] used salient objects' detection and genetic algorithm to learn new knowledge through communicating with humans.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Developmental Cognitive Architecture Based on Incremental Associative Neural Network With Dynamic Audiovisual Fusion

Huang

Song

et al. 2019

IEEE Access

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Developing cognition is difficult to achieve yet crucial for robots. Infants can gradually improve their cognition through parental guidance and self-exploration. However, conventional learning methods for robots often focus on a single modality and train a pre-defined model by large datasets in an offline way. In this paper, we propose a hierarchical autonomous cognitive architecture for robots to learn object concepts online by interacting with humans. Two pathways for audiovisual information are devised. Each pathway has three layers based on the self-organizing incremental neural networks. Visual features and names of objects are incrementally learned and self-organized in an unsupervised way in sample layers, respectively, in which we propose a dynamically adjustable similarity threshold strategy to allow the network itself to control cluster rather than using a pre-defined threshold. Two symbol layers abstract the cluster results from the corresponding sample layer to form concise symbols and transmit them to an associative layer. An associative relationship between two modalities can be built in real time by binding activated visual and auditory symbols simultaneously in the associative layer. In this layer, a top-down response strategy is proposed to let robots autonomously recall another associative modality, solve conflicting associative relationships, and adjust learned knowledge from the top down. The experimental results on two objects datasets and a real task show that our architecture is efficient to learn and associate object view and name in an online way. What is more, the robot can autonomously improve its cognitive level by utilizing its own experience without enquiring with humans. INDEX TERMS Cognitive development, concept online learning, self-organizing incremental neural network, object recognition, audiovisual integration.

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“…To develop a robot that learns from experiences and adapt, several learning systems have been proposed to make it learn from active and passive experiences (Drescher, 1991 ; Montesano et al, 2008 ; Krüger et al, 2011 ; Aguilar and y Pérez, 2015 ; Petit et al, 2016 ; Kansky et al, 2017 ). Drescher ( 1991 ) proposed a learning mechanism based on Piaget's schema mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Also inspired by Piaget's theory, Aguilar and y Pérez ( 2015 ) developed a schema-based learning system called Developmental Engagement-Reflection (Dev-ER) for autonomous agents. Learning consists of schemas that contain preconditions, an action and the postconditions, which are results of applying the action on the preconditions.…”

Section: Introductionmentioning

confidence: 99%

“…The above learning models are comparable to the schema-based mechanism proposed in this work. However, some of these systems do not offer open-ended play behavior (Krüger et al, 2011 ; Kansky et al, 2017 ) and some do not offer planning behaviors to achieve a desired or given state in the environment (Sheldon, 2013 ; Aguilar and y Pérez, 2015 ). Here, we present an intrinsically motivated open-ended learning and play generator system, termed Dev-PSchema.…”

Section: Introductionmentioning

confidence: 99%

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Developing Hierarchical Schemas and Building Schema Chains Through Practice Play Behavior

et al. 2018

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Examining the different stages of learning through play in humans during early life has been a topic of interest for various scholars. Play evolves from practice to symbolic and then later to play with rules. During practice play, infants go through a process of developing knowledge while they interact with the surrounding objects, facilitating the creation of new knowledge about objects and object related behaviors. Such knowledge is used to form schemas in which the manifestation of sensorimotor experiences is captured. Through subsequent play, certain schemas are further combined to generate chains able to achieve behaviors that require multiple steps. The chains of schemas demonstrate the formation of higher level actions in a hierarchical structure. In this work we present a schema-based play generator for artificial agents, termed Dev-PSchema. With the help of experiments in a simulated environment and with the iCub robot, we demonstrate the ability of our system to create schemas of sensorimotor experiences from playful interaction with the environment. We show the creation of schema chains consisting of a sequence of actions that allow an agent to autonomously perform complex tasks. In addition to demonstrating the ability to learn through playful behavior, we demonstrate the capability of Dev-PSchema to simulate different infants with different preferences toward novel vs. familiar objects.

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Dev E-R: A computational model of early cognitive development as a creative process

Cited by 16 publications

References 21 publications

Leaps and Bounds: An Introduction to the Field of Computational Creativity

Leaps and Bounds: An Introduction to the Field of Computational Creativity

An Autonomous Developmental Cognitive Architecture Based on Incremental Associative Neural Network With Dynamic Audiovisual Fusion

Developing Hierarchical Schemas and Building Schema Chains Through Practice Play Behavior

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