Modeling Expert Knowledge in a Heuristic-Based Gin Rummy Agent

Authors

  • Sarah Larkin Michigan Technological University
  • William Collicott Michigan Technological University
  • Jason Hiebel Michigan Technological University

Keywords:

Gin Rummy, Game Playing, Expert Systems, Games With Incomplete Information, Heuristic-Based Model

Abstract

We developed a heuristic-based reflex agent, Tonic, for the EAAI 2021 Undergraduate Research Challenge, which tasks competitors to create an autonomous player to play the card game gin rummy. Tonic's heuristics originate in expert knowledge and inform decision making for the three actions comprising a turn: drawing a card, discarding a card, and deciding when to knock. However, because these strategies are based in human intuition, there is often a lack of specificity to directly model them as algorithms. We developed parameterized models describing that intuition based on factors such as the number of turns played and an estimation of the opponent hand. To hone their performance, we conducted both manual analysis and parameter optimization (grid search) using self-play and play against a simple baseline agent. These heuristic models enable Tonic to win against the baseline agent at least 68% of the time.

Downloads

Published

2021-05-18

How to Cite

Larkin, S., Collicott, W., & Hiebel, J. (2021). Modeling Expert Knowledge in a Heuristic-Based Gin Rummy Agent. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15577-15582. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17834