MCTS Adaptive Split Menu

The original repo of this project : https://github.com/aalto-ui/chi21adaptive

This an extension version of Adaptive Menu, with a focus on adapting the split menu.

My work focused on redesigning the rewards in Deep reinforcement learning to make the whole system work on the split menu specifically.

What is a split menu:

How to adapt:

The items in the recent section is dynamically adpatived to user's preference.

What I have changed:

Files in MCTS - rewards design/ Monte Carlo tree search

What remains as the original repo:

Files in value_network - trained Neural network

The Original Repo:

Project for the MCTS of menu adaptions.

Read the Value Network documentation in a dedicated folder.

Contributors

In alphabetical order:

• Antti Oulasvirta
• Gilles Baily
• Kashyap Todi
• Luis A. Leiva

Requirments

1. Parallel Python (PP): To install for Python3, navigate to the pp-1.6.4.4 directory, and run pip3 install.
2. TensorFlow 2 (https://www.tensorflow.org/install/pip) for using the Value Network

Key Components for MCTS

• plan.py is the starting point for code execution. To generate results, execute:

python3 plan.py -c 5items --nonn The command will run the MCTS planner for the 5 item case (menu_5items.txt) without the value network. To include the value network, remove the --nonn option.

• utility.py contains useful functions for loading data, initialisation, etc.

• Input files are stored within Input folder. For each case, there's an input menu, association list, and user history.

• state.py defines the menu and user state. The root state is initialised using the input menu, associations, and user history

• adaptation.py provides a general format for defining adaptations. It uses the syntax (i,j,type,expose) where i and j are two positions in the menu, type specifies the type of adaptation (e.g. swap, move, group move), and expose is a boolean that specifies whether the adapted menu is exposed to the user or not.

• mcts.py contains the entire code for running the Monte Carlo tree search.

• useroracle.py defines the user models for running simulations. These models are used towards predicting task completion time given a menu design, and for computing the reward after making an adaptation.