Paper Info
Title
Beyond playing to win: Diversifying heuristics for GVGAI
Abstract
General Video Game Playing (GVGP) algorithms are usually focused on winning and maximizing score but combining different objectives could turn out to be a solution that has not been deeply investigated yet. This paper presents the results obtained when five GVGP agents play a set of games using heuristics with different objectives: maximizing winning, maximizing exploration, maximizing the discovery of the different elements presented in the game (and interactions with them) and maximizing the acquisition of knowledge in order to accurately estimate the outcome of each possible interaction. The results show that the performance of the agents changes depending on the heuristic used. So making use of several agents with different goals (and their pertinent heuristics) could be a feasible approach to follow in GVGP, allowing different behaviors in response to the diverse situations presented in the games.
Year
DOI
Venue
2017
10.1109/CIG.2017.8080424
2017 IEEE Conference on Computational Intelligence and Games (CIG)
Keywords
Field
DocType
GVGAI,GVGP agents,pertinent heuristics,general video game playing algorithms
Heuristic,Monte Carlo method,Computer science,Simulation,Evolutionary computation,Heuristics,Artificial intelligence,Machine learning,General video game playing
Conference
ISBN
Citations 
PageRank 
978-1-5386-3234-5
2
0.36
References 
Authors
12
3
Name
Order
Citations
PageRank
Cristina Guerrero-Romero120.36
Annie Louis244324.78
diego perez320226.00