More Like this

1. Mech-Elites: Illuminating the Mechanic Space of GVG-AI

   https://doi.org/10.1145/3402942.3402954  

   Charity, Megan; Green, Michael Cerny; Khalifa, Ahmed; Togelius, Julian (September 2020, International Conference on the Foundations of Digital Games)

   null (Ed.)

   This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate the mechanic space for four different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals. With this system, we can generate playable levels that contain different combinations of most of the possible mechanics. These levels can later be used to populate game tutorials that teach players how to use the mechanics of the game. 

   more » « less
2. A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

   https://doi.org/10.1109/CEC48606.2020.9185834  

   Stephenson, Matthew; Anderson, Damien; Khalifa, Ahmed; Levine, John; Renz, Jochen; Togelius, Julian; Salge, Christoph (July 2020, IEEE Congress on Evolutionary Computation)

   null (Ed.)

   This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways. 

   more » « less
3. Mario Level Generation From Mechanics Using Scene Stitching

   https://doi.org/10.1109/CoG47356.2020.9231692  

   Green, Michael Cerny; Mugrai, Luvneesh; Khalifa, Ahmed; Togelius, Julian (August 2020, IEEE Conference on Games)

   null (Ed.)

   Video game tutorials allow players to gain mastery over game skills and mechanics. To hone players’ skills, it is beneficial from practicing in environments that promote individ- ual player skill sets. However, automatically generating environ- ments which are mechanically similar to one-another is a non- trivial problem. This paper presents a level generation method for Super Mario by stitching together pre-generated “scenes” that contain specific mechanics, using mechanic-sequences from agent playthroughs as input specifications. Given a sequence of mechanics, the proposed system uses an FI-2Pop algorithm and a corpus of scenes to perform automated level authoring. The proposed system outputs levels that can be beaten using a similar mechanical sequence to the target mechanic sequence but with a different playthrough experience. We compare the proposed system to a greedy method that selects scenes that maximize the number of matched mechanics. Unlike the greedy approach, the proposed system is able to maximize the number of matched mechanics while reducing emergent mechanics using the stitching process. 

   more » « less
4. Experiments with sensorimotor games in dynamic human/machine interaction

   https://doi.org/10.1117/12.2519258  

   Chasnov, Benjamin; Yamagami, Momona; Parsa, Behnoosh; Ratliff, Lillian J.; Burden, Samuel A. (May 2019, Micro- and Nanotechnology Sensors, Systems, and Applications XI)

   While interacting with a machine, humans will naturally formulate beliefs about the machine's behavior, and these beliefs will affect the interaction. Since humans and machines have imperfect information about each other and their environment, a natural model for their interaction is a game. Such games have been investigated from the perspective of economic game theory, and some results on discrete decision-making have been translated to the neuromechanical setting, but there is little work on continuous sensorimotor games that arise when humans interact in a dynamic closed loop with machines. We study these games both theoretically and experimentally, deriving predictive models for steady-state (i.e. equilibrium) and transient (i.e. learning) behaviors of humans interacting with other agents (humans and machines). Specifically, we consider experiments wherein agents are instructed to control a linear system so as to minimize a given quadratic cost functional, i.e. the agents play a Linear-Quadratic game. Using our recent results on gradient-based learning in continuous games, we derive predictions regarding steady-state and transient play. These predictions are compared with empirical observations of human sensorimotor learning using a teleoperation testbed. 

   more » « less
5. “Press Space To Fire”: Automatic Video Game Tutorial Generation

   Green, Michael Cerny; Khalifa, Ahmed; Barros, Gabriella A.; Togelius, Julian (January 2017, Workshop on Experimental AI in Games)

   We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem. 

   more » « less