The Multi-Perspective Context Matching model introduced by Wang, et al. [1] in 2016 is known to be capable of producing strong results in the SQuAD question answering task. As of this writing, it is tied for 3 place on the SQuAD leaderboard. Implementing the model efficiently is difficult in practice, and the original introduction paper leaves out some implementation details. The goal of this p...

The SQuad data structure represents the connectivity of a triangle mesh by its “S table” of about 2 rpt (integer references per triangle). Yet it allows for a simple implementation of expected constant-time, random-access operators for traversing the mesh, including in-order traversal of the triangles incident upon a vertex. SQuad is more compact than the Corner Table (CT), which stores 6 rpt, ...

Consider a fully connected network where up to t processes may crash, and all processes start in an arbitrary memory state. The self-stabilizing firing squad problem consists of eventually guaranteeing simultaneous response to an external input. This is modeled by requiring that the non-crashed processes “fire” simultaneously if some correct process received an external “go” input, and that the...

The value of socializing in collocated multiplayer video games should not be underestimated. Inter-player interactions such as joking, bantering and strategizing all contribute towards creating rewarding and memorable gameplay experiences for the players involved. In this paper, we introduce Prism Squad: GO!, a cooperative, three-player video game which is designed to encourage and cultivate th...

Most modern real-time strategy computer games have a sophisticated but fixed ‘AI’ component that controls the computer’s actions. Once the user has learned how such a game will react, the game quickly loses its appeal. This paper describes an example of how a learning classifier system (based on Wilson’s ZCS [1]) can be used to equip the computer with dynamically-changing strategies that respon...