This tutorial gives an overview of the lecture about narrative enabled episode memories (NEEMS) by Sebastian Koralewski.

Nowadays machine learning is applicable for a large variety of cases. This lecture shows how to use the recordings of activities performed by a robot in a kitchen environment to predict a likely course of action, based on the observed data and their probability of success, e.g. setting up a table for breakfast, or cleaning it up afterward.

The platform of choice is Jupyter Notebook, a handy framework that is ready to launch in any python environment. Please find the repository's README for a setup manual. Within the Jupyter environment, there are explanations on how to solve the tasks at hand. Most of the implementation utilizes the Pandas and sklearn package, which makes analyzing and learning data easily possible.

Some example data is provided with this tutorial in a CSV format. They contain records of robot activities, the so-called NEEMS.

Link to the lecture.

In the browser, the filesystem of the repository is shown. Click on the Exercises.ipynb to get to the tutorial. The code snippets can be executed with Ctrl-Enter or by hitting the Run button in the navigation bar at the top. The code needs to be executed step by step and the program state is kept throughout the tutorial, such that earlier declared variables can be used later.

This lecture is granulated into six consecutive sections. Since the Jupyter Notebook is a python script itself, it is important to do each section in the given order. If you separate the lecture over several sessions, remember to execute the code from previous sections before continuing your work.

• 1. Data Analysis - Visualizing the Data
• 2. Data Preparation
• 3. Brief Introduction to Decision Trees
• 4. Additional Machine Learning Theory
• 5. Train the Next Action Classifier
• 6. Evaluate the Next Action Classifier