Hello! Our presentation is Moving Architecture Animated Maze: The Intertextuality Between Player
and Environment Agents. I'm Yiou, an architect, game designer, and researcher from Harvard
Graduate School of Design. I'm Yujie, and I'm an interaction designer and creative
technologist from MIT. The excessive nature of the historical maze gave us our initial idea.
Maze introduced choice and indeterminacy the maze belongs to the Ilinx theorized by
Roger Caillois - which is based on player navigation in disorientation. In modern game classification,
the game maps onto the puzzle genre which activates problem-solving skills based on
logic and spatial cognition. The basic geometry of the maze is a sprawling horizontal structure
of orthogonal walls that generate a space of possibility. On the micro level, the maze
contains a multitude of this single unit rotated in different orientations. On the meso level,
the maze is semiotic the units cluster together through various combinations and permutations
forming these four types the corner, the passage, the junction, and the dead end. On the macro level,
the maze is generated and continuously generating space of possibilities from propagation and
rotation. Games are anchored on rules, we want to design a game that is able to operate itself
once rules are established. The novel rule that separates this game from the rest of
maze games is based on alternative feedback mechanics after every move the player takes
the maze reconfigures in response to the player's movement space evokes and constructs a narrative
offering indexical narrative elements that provide spatial comprehension for the player. Instead of
explicit instructions that tell the player how to move forward, the game opts for having the player
discover the pattern through indices and learn to navigate the moving maze with a difficulty
progression from easy to hard. In the game there are two agents, the player and the environment
agent, which is the maze. On the superficial level, the player aims to navigate out of the maze
while the maze reconfigures to abstract player from finding a solution pass the oppositional
goals create a zero-sum situation of the player playing against maze. But unlike AlphaGo that is
built to win, the ultimate goal of the movie maze as a video game is to immerse the human player
in an engaging rich game experience rather than defeat the human player. The environment agent
would simultaneously obstruct the human player and mitigate this action to match the level of
the player enable them a successful exit most of the time, but remain challenging to them with the
probability to let them fail. Therefore, the moving maze is not just a human-machine zero-sum game but
also a non zero-sum game whose game mechanics is explicitly adversarial but implicitly cooperative.
To program the maze that adapts to the level of the player, a set of Game State parameters are
defined to determine the difficulty for a player to get out of the moving maze. Player-dependent
parameters also factor into the quantification of the game to evaluate the player experience. We
propose that the Difficulty Index of a solution path is a weighted sum of game state parameters mentioned
above. A smooth difficulty progression is quantified as an incremental increase of Difficult Index while
leveling up. It is also important to notice that these Game State parameters are interdependent.
To better approximate the weight of each parameter, playtest data through inverse reinforcement
learning at a statistically meaningful scale are needed. To evaluate playability and player
engagement, we conducted preliminary desktop playtests with 5 players, the results are shown below,
which achieved our initial goal in difficulty progression. The concept of the maze as a game of
architecture raises from its spatial potential to engender modes of play, measured on player
engagement, which involves game mechanics and balanced player agency and environment agency.
Game balance is the fine-tuning of the game to be engaging, moderately challenging, and often
manageable with incremental race in difficulty. Game balancing is often a time-consuming process
and new learning algorithms have recently been used to make this process more efficient.
In our initial moving maze game prototype, we propose to introduce adversarial reinforcement
learning in this multi-agent environment to improve competitive game mechanics
Thank you for listening, the movie maze is playable at Itch, and our team is working
on developing the game onto the next level. So stay tuned!