Towards a Brain Inspired Model of Self-Awareness for Sociable Agents
Authors: Budhitama Subagdja, Ah-Hwee Tan
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The model is built and realized on a NAO humanoid robotic platform to investigate the role of this capacity of self-awareness on the robot s learning and interactivity. |
| Researcher Affiliation | Academia | Budhitama Subagdja Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Nanyang Technological University, Singapore e-mail: budhitama@ntu.edu.sg Ah-Hwee Tan School of Computer Science and Engineering Nanyang Technological University, Singapore e-mail: asahtan@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: Creating a deeper level of awareness |
| Open Source Code | No | The paper does not provide any specific link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | No | The paper describes the NAO robot's built-in mechanisms and its ability to learn from interactions ('It can identify a person based on one s facial features that have been learnt before...'). However, it does not mention the use of a formal publicly available or open dataset for training, nor does it provide any concrete access information (link, DOI, specific citation) for any data used in training. |
| Dataset Splits | No | The paper does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper states that the model is applied to a 'NAO humanoid robotic platform' and provides a URL for it, but does not specify any detailed hardware components like CPU models, GPU models, or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions 'programmable voice-recognition system' and 'built-in mechanisms' for the NAO robot but does not provide specific names or version numbers for any software dependencies required to reproduce the work. |
| Experiment Setup | No | The paper describes the scenarios and the general integration with the NAO platform, but it does not provide concrete experimental setup details such as hyperparameter values (e.g., learning rate, batch size), optimizer settings, or other system-level training configurations. |