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xaiexemplar

Explaining Tele Assist System (TAS) workflow adaptations using LIME

https://github.com/shivshringare/xaiexemplar

Science Score: 44.0%

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    Low similarity (6.5%) to scientific vocabulary

Keywords

adaptive-systems lime reinforcement-learning xai
Last synced: 6 months ago · JSON representation ·

Repository

Explaining Tele Assist System (TAS) workflow adaptations using LIME

Basic Info
  • Host: GitHub
  • Owner: shivshringare
  • Language: HTML
  • Default Branch: main
  • Homepage:
  • Size: 14 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
adaptive-systems lime reinforcement-learning xai
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Citation

README.md

Explaining Tele Assist System (TAS) workflow adaptations using LIME

Source Code for Auckland University of Technology Master's 60pt Dissertation

Before you begin, ensure you have met the following requirements: - Python 3.11 or later - Pip package manager - An environment manager like venv (optional, but recommended)

Setup Python Virtual Environment

python -m venv env

Activate the virtual environment

On Windows:

venv\Scripts\activate

On Linux or MacOS:

source venv/bin/activate

Setup

Install dependencies

pip install <dependency>

Usage

  • Run explanable_tas.py to run the workflow and generate LIME explanations and plots

References

  • https://www.datacamp.com/tutorial/random-forests-classifier-python
  • https://www.geeksforgeeks.org/multi-armed-bandit-problem-in-reinforcement-learning/
  • https://marcotcr.github.io/lime/tutorials/Tutorial%20-%20continuous%20and%20categorical%20features.html

Owner

  • Name: Shiv Shringare
  • Login: shivshringare
  • Kind: user
  • Location: Mumbai, India
  • Company: Codebox

Software Engineer

Citation (CITATION.cff) 

cff-version: 1.2.0
title: Explaining Tele Assist System (TAS) workflow adaptations using LIME
type: software
year: 2024
authors:
  - given-names: Shiv
    family-names: Shringare
    affiliation: Auckland University of Technology
repository-code: 'https://github.com/shivshringare/xaiexemplar'

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