Please note: This PhD seminar will be given online.
Josh
Jung,
PhD
candidate
David
R.
Cheriton
School
of
Computer
Science
Supervisor: Professor Jesse Hoey
In the field of General Game Playing (GGP), artificial agents (bots) may be required to play never-before-seen games with less than one minute to initialize and train. Although tabula rasa approaches, like Monte-Carlo Tree Search, are popular in this domain, they do not leverage information from the many different games that a bot has previously encountered. A major barrier to transfer learning has been the difficulty in identifying similar features in the rule descriptions of two different games. We present two methods, called MMap and LMap, for heuristically approximating a distance between two games' graphs, and producing a mapping for the symbols of one to the other, thereby enabling transfer. We evaluate the effectiveness of these methods across a variety of transfer scenarios, and find that both methods are far more accurate than a simpler baseline mapper. MMap is found to be more robust than LMap, but LMap is much faster, and so more suitable for general use in GGP.
To join this PhD seminar on Zoom, please go to https://uwaterloo.zoom.us/j/97625755857?pwd=VGU3T2lpSXhlRmU2QzE5R0hSMklFdz09.