Beyond playing to win: Diversifying heuristics for GVGAI

摘要

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.