General Video Game AI: Competition, Challenges and Opportunities
Authors: Diego Perez-Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon Lucas
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This short paper summarizes the motivation, infrastructure, results and future plans of General Video Game AI, stressing the findings and first conclusions drawn after two editions of our competition, and outlining our future plans. |
| Researcher Affiliation | Academia | Diego Perez-Liebana University of Essex Colchester CO4 3SQ, UK email: dperez@essex.ac.uk; Spyridon Samothrakis University of Essex Colchester CO4 3SQ, UK email: ssamot@essex.ac.uk; Julian Togelius New York University, 2 Metrotech Center Brooklyn, 11201 New York email: julian@togelius.com; Simon M. Lucas University of Essex Colchester CO4 3SQ, UK email: sml@essex.ac.uk; Tom Schaul New York University 715 Broadway, 10003, New York email: schaul@gmail.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper refers to a competition server (www.gvgai.net) but does not provide concrete access to source code for the methodology or framework itself. |
| Open Datasets | No | The paper mentions games implemented in VGDL and sets of available games for training, but it does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year) for these datasets. |
| Dataset Splits | Yes | competitors use on one or more sets of available games to train on (all games are provided with 5 different levels). Additionally, there is a competition server1 set up to receive submissions and run the controllers on an unknown set of 10 games for validation. ... The final rankings are obtained from the execution of all submissions in a third (secret) set of 10 games. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The GVGAI framework, written in Java, provides an object-oriented interface for creating agents that can play in any game defined in VGDL. |
| Experiment Setup | Yes | After a short initialization phase, the agent receives calls at every game step and must return a discrete action to apply in no more than 40ms. The agent receives information about the game state via a Java object. This object allows the agent to query the game status (winner, time step, score), the player s state (position, orientation, resources), history of events or collisions during the game, and position of the different sprites in the level, identiļ¬ed only by an integer id for its type. |