Synthesizing Game Levels for Collaborative Gameplay in a Shared Virtual Environment

Abstract

We developed a method to synthesize game levels that accounts for the degree of collaboration required by two players to finish a given game level. We first asked a game level designer to create playable game level chunks. Then, two artificial intelligence (AI) virtual agents driven by behavior trees played each game level chunk. We recorded the degree of collaboration required to accomplish each game level chunk by the AI virtual agents and used it to characterize each game level chunk. To synthesize a game level, we assigned to the total cost function cost terms that encode both the degree of collaboration and game level design decisions. Then, we used a Markov-chain Monte Carlo optimization method, called simulated annealing, to solve the total cost function and proposed a design for a game level. We synthesized three game levels (low, medium, and high degrees of collaboration game levels) to evaluate our implementation. We then recruited groups of participants to play the game levels to explore whether they would experience a certain degree of collaboration and validate whether the AI virtual agents provided sufficient data that described the collaborative behavior of players in each game level chunk. By collecting both in-game objective measurements and self-reported subjective ratings, we found that the three game levels indeed impacted the collaboration gameplay behavior of our participants. Moreover, by analyzing our collected data, we found moderate and strong correlations between the participants and the AI virtual agents. These results show that game developers can consider AI virtual agents as an alternative method for evaluating the degree of collaboration required to finish a game level.