Please note: This PhD seminar will take place in DC 2310 (not in DC 2584 as originally advertised) and virtually over Zoom.
Joshua Jung, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jesse Hoey
General Game Playing (GGP) is a field of study in which artificial agents are required to compete in games whose rules are not known until runtime. In this domain, time is at a premium, as there may be less than a minute for an agent to initialize and decide on each of its actions. As a result, Monte Carlo Tree Search (MCTS) has been favoured by researchers in this domain for its ability to run in whatever time it is given, by quickly simulating many instances of a game. Heuristics may be used to guide these simulations, but since the game is not known in advance, a typical MCTS agent cannot know which heuristics are likely to be useful, and must expend precious time in trying to discover them. However, an agent able to take advantage of knowledge gained from prior experience with other, different, games, can do better.
We present a technique for automatically transferring heuristic knowledge between distinct, but similar, games. We show that using this knowledge to improve the quality of game-independent heuristics can produce better performance in games within the GGP framework, especially when initialization time is short.
To attend this seminar in person, please go to DC 2310. You can also attend virtually using Zoom at https://uwaterloo.zoom.us/j/93961737021.
200 University Avenue West
Waterloo, ON N2L 3G1