Dataset Information

Integrated Cognitive Architecture for Robot Learning of Action and Language.

ABSTRACT: The manner in which humans learn, plan, and decide actions is a very compelling subject. Moreover, the mechanism behind high-level cognitive functions, such as action planning, language understanding, and logical thinking, has not yet been fully implemented in robotics. In this paper, we propose a framework for the simultaneously comprehension of concepts, actions, and language as a first step toward this goal. This can be achieved by integrating various cognitive modules and leveraging mainly multimodal categorization by using multilayered multimodal latent Dirichlet allocation (mMLDA). The integration of reinforcement learning and mMLDA enables actions based on understanding. Furthermore, the mMLDA, in conjunction with grammar learning and based on the Bayesian hidden Markov model (BHMM), allows the robot to verbalize its own actions and understand user utterances. We verify the potential of the proposed architecture through experiments using a real robot.

SUBMITTER: Miyazawa K

PROVIDER: S-EPMC7805838 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Integrated Cognitive Architecture for Robot Learning of Action and Language.

Miyazawa Kazuki K Horii Takato T Aoki Tatsuya T Nagai Takayuki T

Frontiers in robotics and AI 20191129

The manner in which humans learn, plan, and decide actions is a very compelling subject. Moreover, the mechanism behind high-level cognitive functions, such as action planning, language understanding, and logical thinking, has not yet been fully implemented in robotics. In this paper, we propose a framework for the simultaneously comprehension of concepts, actions, and language as a first step toward this goal. This can be achieved by integrating various cognitive modules and leveraging mainly m ...[more]

PMID: 33501146

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Project description:Environmental enrichment (EE) conditions have profound beneficial effects for reinstating cognitive ability in neuropathological disorders like Alzheimerâs disease (AD). While EE benefits involve epigenetic gene control mechanisms that comprise histone acetylation, the histone acetyltransferases (HATs) involved remain largely unknown. Here, we examine a role for Tip60 HAT action in mediating activity- dependent beneficial neuroadaptations to EE using the Drosophila CNS mushroom body (MB) as a well-characterized cognition model. We show that flies raised under EE conditions display enhanced MB axonal outgrowth, synapse protein production, histone acetylation induction and transcriptional activation of cognition linked genes when compared to their genotypically identical siblings raised under isolated conditions. Further, these beneficial changes are impaired in both Tip60 HAT mutant flies and APP neurodegenerative flies. While EE conditions provide only slight beneficial neuroadaptive changes in the APP neurodegenerative fly MB, such positive changes are significantly enhanced by increasing MB Tip60 HAT levels. Our results implicate Tip60 as a critical mediator of EE-induced benefits, and provide insight into synergistic behavioral and epigenetic based approaches for treatment of cognitive disorders. EE has been shown to positively impact gene expression profiles in the mouse brain that are enriched in functions such as neuronal structure, synaptic plasticity and neurotransmission. Thus, we asked whether the EE induced beneficial MB structural and synaptic changes we observe are accompanied by neuroadaptive transcriptional benefits in the Drosophila MB, and if so, is Tip60 HAT action required for this process. To address this question, we accessed EE induced beneficial transcriptional changes using microarray. We crossed our UAS-mCD8-GFP;Tip60E431Q flies or control UAS-mCD8-GFP flies to MB GAL4 OK-107 to simultaneously induce Tip60 HAT loss in the MB while tagging MB cells with GFP. Adult progeny were exposed to EE or ISO conditions. After conditioning, the GFP tagged MB Kenyon neurons were FACs purified from conditioned fly brains from each genotype to enrich for detection of an EE induced MB transcriptional response. RNA was isolated from the purified Kenyon MB neurons and transcriptional changes for each genotype were assessed using microarray analysis

Dataset Information

Integrated Cognitive Architecture for Robot Learning of Action and Language.

Publications

Integrated Cognitive Architecture for Robot Learning of Action and Language.

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