CITATION.cff

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Towards Action Model Learning for Player Modeling
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Abhijeet
    family-names: Krishnan
    email: akrish13@ncsu.edu
    affiliation: North Carolina State University
    orcid: 'https://orcid.org/0000-0002-0463-5105'
  - given-names: Aaron
    family-names: Williams
    email: amwill19@ncsu.edu
    affiliation: North Carolina State University
  - given-names: Chris
    family-names: Martens
    email: martens@csc.ncsu.edu
    affiliation: North Carolina State University
    orcid: 'https://orcid.org/0000-0002-7026-0348'
identifiers:
  - type: url
    value: >-
      https://ojs.aaai.org/index.php/AIIDE/article/view/7436
repository-code: >-
  https://github.com/AbhijeetKrishnan/aml-for-player-modeling
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 action model
  learning (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.
keywords:
  - 'action model learning, player modeling'
license: CC-BY-3.0
commit: 88a5d8aefc6c7bf73ccff3fa13f83d448d9df149
version: '1.0'
date-released: '2021-03-02'