Adapting User Interfaces with Model-based Reinforcement Learning (ACM CHI 2021)

This code repository is for the adaptive menus application described in the CHI 2021 paper: https://userinterfaces.aalto.fi/adaptive/resources/chi2021-todi-adaptive.pdf

Requirements

Ray (https://ray.io) for parallelisation.
TensorFlow 2 (https://www.tensorflow.org/install/pip) for using the neural networks.

plan.py is the starting point for code execution. To generate results, execute:
python3 plan.py The command will run the MCTS planner for the 5-item case (menu_5items.txt) without the value network. To use the value network, add the -nn option.
python3 plan.py -h See the full list of options available for running plan.py
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 code for 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.