Modeling of Action’s Semantic Memory Incorporated with Procedural and Skill Memory to Perform Tasks
Rahul Shrivastava1, Prabhat Kumar2, Sudhakar Tripathi3
1Rahul Shrivastava*, Department of Computer Science & Engineering, National Institute of Technology, Patna, Bihar, India.
2Prabhat Kumar, Department of Computer Science & Engineering, National Institute of Technology, Patna, Bihar, India.
3Sudhakar Tripathi, Department of Computer Science & Engineering Rajkiya Engineering College Ambedkar Nagar, UP, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1014-1024 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4227098319/19©BEIESP | DOI: 10.35940/ijrte.C4227.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, a computational model is proposed to mimic an action’s semantic, procedural and skill learning’s by an abstract modeling of cortical columns of the Neocortex, Basal ganglia and Cerebellum brain region. In proposed work, the action semantic Learning makes a robot capable to learn an action in terms of their body parts movement sequence that allows it to recognize the learnt action by seeing as well. Whereas in procedural, it allows to learn tasks in the form of action’s hierarchy and makes it capable to capture the environmental features as a context for action’s activations. The skill memory also been added in the proposed work which allows an agent to translate the action as per the current demand of the action. Also, the model has used Vnect model of computer vision to map the human motion into sequence of 3D skeleton of human body, therefore the model can learn by seeing, like humans. In experimental work, the model is tested on vague samples of few actions, where the model is found robust in action recognition task and performed well as per the expectations.
Keywords: Action Semantic learning, hierarchical procedural learning, motor skill learning.
Scope of the Article: Semantic Web