JSON-Bag: A generic game trajectory representation
Dien Nguyen, Diego Perez-Liebana, Simon Lucas

TL;DR
JSON-Bag is a novel, generic method for representing game trajectories using tokenized JSON descriptions and Jensen-Shannon distance, enabling effective classification and feature extraction across various tabletop games.
Contribution
The paper introduces JSON-Bag, a new approach for representing game trajectories that improves classification accuracy and sample efficiency over hand-crafted features.
Findings
JSON-Bag outperforms hand-crafted features in most classification tasks.
Using JSON-Bag prototypes enhances sample efficiency in N-shot classification.
JSD between JSON-Bag prototypes correlates with policy distances across games.
Abstract
We introduce JSON Bag-of-Tokens model (JSON-Bag) as a method to generically represent game trajectories by tokenizing their JSON descriptions and apply Jensen-Shannon distance (JSD) as distance metric for them. Using a prototype-based nearest-neighbor search (P-NNS), we evaluate the validity of JSON-Bag with JSD on six tabletop games: 7 Wonders, Dominion, Sea Salt and Paper, Can't Stop, Connect4, Dots and boxes; each over three game trajectory classification tasks: classifying the playing agents, game parameters, or game seeds that were used to generate the trajectories. Our approach outperforms a baseline using hand-crafted features in the majority of tasks. Evaluating on N-shot classification suggests using JSON-Bag prototype to represent game trajectory classes is also sample efficient. Additionally, we demonstrate JSON-Bag ability for automatic feature extraction by treating…
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Taxonomy
TopicsArtificial Intelligence in Games · Digital Games and Media · Reinforcement Learning in Robotics
