EAs + Monte Carlo Tree Search for the win (*BEST PAPER of EvoAPPS 2020 conference*)

Yes! We did it! :D

Yesterday we presented our last paper on the scope of AI in Videogames entitled “Testing hybrid computational intelligence algorithms for general game playing“, by two colleagues of the University of Málaga (A. E. Vázquez-Núñez and A. J. Fernández-Leiva), together with P. García-Sánchez (@fergunet) and I (A. M. Mora).

It was focused on the domain of General Game Playing, where the aim is to create an autonomous agent able to adapt itself to play to several different videogames (not previously known for it). We used the framework and followed the rules of the General Video Game AI Competition.

Thus, it’s an approach to implement a kind of human-like behaviour the first time we face a new game.

The manuscript was selected as the Best Paper of EvoAPPS 2020 conference (inside EVO* 2020), held online for the first time, due to COVID-19 situation.

The abstract of the work is:

General Videogame Playing is one of the hottest topics in the research field of AI in videogames. It aims at the implementation of algorithms or autonomous agents able to play a set of unknown games efficiently, just receiving the set of rules to play in real time. Thus, this work presents the implementation of eight approaches based on the main techniques applied in the literature to face this problem, including two different hybrid implementations combining Monte Carlo Tree Search and Genetic Algorithms. They have been created within the General Video Game Artificial Intelligence (GVGAI) Competition platform. Then, the algorithms have been tested in a set of 20 games from that competition, analyzing its performance. According to the obtained results, we can conclude that the proposed hybrid approaches are the best approaches, and they would be a very competitive entry for the competition.

And here is the presentation:

Enjoy it!

And, as usual, cite us. ;D

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