Search Results for author: Deokgun Park

Found 6 papers, 0 papers with code

Hippocampus-Inspired Cognitive Architecture (HICA) for Operant Conditioning

no code implementations16 Dec 2022 Deokgun Park, Md Ashaduzzaman Rubel Mondol, Sm Mazharul Islam, Aishwarya Pothula

HICA explains a learning mechanism in which agents can learn a new behavior policy in a few trials, as mammals do in operant conditioning experiments.

Hippocampus

Toward Human-Level Artificial Intelligence

no code implementations9 Aug 2021 Deokgun Park

In this paper, we present our research on programming human-level artificial intelligence (HLAI), including 1) a definition of HLAI, 2) an environment to develop and test HLAI, and 3) a cognitive architecture for HLAI.

Hippocampus

Modeling Social Interaction for Baby in Simulated Environment for Developmental Robotics

no code implementations29 Dec 2020 Md Ashaduzzaman Rubel Mondol, Aishwarya Pothula, Deokgun Park

It simulates the environments for a baby agent that a human baby experiences throughout the pre-born fetus stage to post-birth 12 months.

A Definition and a Test for Human-Level Artificial Intelligence

no code implementations18 Nov 2020 Deokgun Park, Md Ashaduzzaman Rubel Mondol, Aishwarya Pothula, Mazharul Islam

Despite recent advances of AI research in many application-specific domains, we do not know how to build a human-level artificial intelligence (HLAI).

Language Acquisition

SEDRo: A Simulated Environment for Developmental Robotics

no code implementations3 Sep 2020 Aishwarya Pothula, Md Ashaduzzaman Rubel Mondol, Sanath Narasimhan, Sm Mazharul Islam, Deokgun Park

Even with impressive advances in application-specific models, we still lack knowledge about how to build a model that can learn in a human-like way and do multiple tasks.

An Open-World Simulated Environment for Developmental Robotics

no code implementations18 Jul 2020 SM Mazharul Islam, Md Ashaduzzaman Rubel Mondol, Aishwarya Pothula, Deokgun Park

As the current trend of artificial intelligence is shifting towards self-supervised learning, conventional norms such as highly curated domain-specific data, application-specific learning models, extrinsic reward based learning policies etc.

Self-Supervised Learning

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