Name
Abstract | ||
|---|---|---|
Imitating video game players is considered one of the most stimulating challenges for the Game AI research community. The goal for a virtual player is not just to beat the game but to show some human-like playing style. In this work we describe a Case-Based Reasoning approach that learns to play the popular Ms. Pac-Man vs Ghosts video game from the traces of a human player. We evaluate the performance of our bot using both low level standard measures such as accuracy and recall, and high level measures such as recklessness (distance to the closest ghost, as it is mapped in our Ms. Pac-Man domain model), restlessness (changes of direction), aggressiveness (ghosts eaten), clumsiness (game steps the player is stuck) and survival (lives left). Results suggest that, although there is still a lot of room for improvement, some aspects of the human playing style are indeed captured in the cases and used by our bot. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01081-2_20 | ICCBR |
Keywords | Field | DocType |
Virtual video game player,Human behavior imitation,Case-based reasoning,Artificial Neural Network,Artificial Intelligence | Computer science,Recklessness,Clumsiness,Artificial intelligence,Artificial neural network,Case-based reasoning,Recall,Machine learning,Domain model | Conference |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maximiliano Miranda | 1 | 0 | 0.68 |
| Antonio A. Sánchez-Ruiz | 2 | 0 | 2.37 |
| Federico Peinado | 3 | 232 | 23.49 |
| Sánchez-Ruiz Antonio A. | 4 | 0 | 0.34 |