abhijeetkrishnan / aml-for-player-modeling

Code repository for the paper 'Towards Action Model Learning for Player Modeling' by Krishnan, Williams and Martens, published in the Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. Vol. 16. No. 1. 2020.

Towards Action Model Learning for Player Modeling

Code repository for the paper titled Toward Action Model Learning for Player Modeling by Abhijeet Krishnan, Aaron Williams and Dr. Chris Martens [link]

This repository may be under active development. Please use the version tagged v1.0 to replicate the results from the paper. You may use the bash commands below to checkout the correct version.

git fetch --all --tags
git checkout tags/v1.0

Feasibility Evaluation

In this, we show that AML is a feasible approach to player modeling by successfully learning a player model using AML

Install FAMA

1. Clone their repo
2. Follow the installation instructions in their README

Sokoban PDDL Domain

• Taken from the Potassco pddl-instances repo
• File located here
• All code related to action costs have been removed from the domain and instances
• The empty domain has been created from the original by removing the preconditions and effects for every action

PUCRS PDDL Parser code

• Taken from their repo
• Uses the PDDL.py and action.py files

Instructions for reproducing results

1. Install FAMA locally
2. Obtain the Potassco Sokoban PDDL domain
3. Convert the custom levels into PDDL instances using ./instances_gen levels instances
4. Run FastDownward to generate the solution files for each instance using ./soln_file_gen.sh reference-sokoban.pddl instances solutions
5. Generate trajectories for each solution using ./traj_file_gen.sh reference-sokoban.pddl instances solutions logs
6. Run FAMA to obtain an action model for each trajectory using ./run_fama.py empty-sokoban.pddl logs models

Usefulness Evaluation

In this, we show that AML is a useful approach to player modeling by quantifying player skill using a learned player model

Instructions for reproducing results

1. Learn a player model following the steps in the feasibility evaluation 2 . Run evaluation for each action model using ./trajectory/evaluation.py reference-sokoban.pddl [PATH/TO/MODEL]

Domain-agnosticity Evaluation

In this, we show that AML is a domain-agnostic approach to player modeling by successfully learning player models in two different domains

Instructions for reproducing results

1. Repeat step 6 in the feasibility evaluation but replace *-sokoban.pddl with *-hanoi.pddl and *-npuzzle.pddl in two separate runs
2. Use the first two trajectories (trajectory-00 and trajectory-01) in the FAMA evaluation dataset corresponding to these two domains as test trajectories

Comparing Blackout and FAMA for Player Modeling

In this, we compare Blackout, our novel algorithm for AML, with FAMA, a SOTA algorithm for AML, for their suitability to player modeling

Learned Model Evaluation

1. Locate the instances L1, L2 and L3 under blackout/trajectory as small-*.pddl, medium-*.pddl and large-*.pddl respectively (the verbose variants contained failed actions which only Blackout can use)
2. Learn 3 models using FAMA following the steps in the feasibility evaluation
3. Learn 3 models using Blackout following the steps below for each of L1, L2 and L3 -
   1. Run Blackout stage 1 using python blackout1.py trajectory/[size]-verbose.log model/[size]-blackout-1.pddl sokoban-sequential in the blackout/ folder
   2. Run Blackout stage 2 using python blackout2.py trajectory/[size]-verbose.log model/[size]-blackout-2.pddl sokoban-sequential in the blackout/ folder
   3. Run Blackout stage 3 using python blackout3.py trajectory/[size]-verbose.log model/[size]-blackout-3.pddl sokoban-sequential in the blackout/ folder
4. Run the evaluation script to obtain precision and recall values for each learned model using python evaluation.py reference-sokoban.pddl path/to/model in the trajectory folder (not to be confused with blackout/trajectory)

Performance Evaluation

The evaluation procedure is described in sufficient detail in the paper for reproducibility

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If you wish to cite the original paper that uses this repository, please use

@article{Krishnan_Williams_Martens_2021, 
    title={Towards Action Model Learning for Player Modeling}, 
    volume={16}, 
    url={https://ojs.aaai.org/index.php/AIIDE/article/view/7436}, 
    number={1}, 
    journal={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, 
    author={Krishnan, Abhijeet and Williams, Aaron and Martens, Chris}, 
    year={2021}, 
    month={Apr.}, 
    pages={238-244},
    abstractNote={<p class="abstract">Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using <em>action model learning</em> (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.</p>} }