Search

Fall 2009

Silver, David

Learning and planning are two fundamental problems in artificial intelligence. The learning problem can be tackled by reinforcement learning methods, such as temporal-difference learning, which update a value function from real experience, and use function approximation to generalise across...

Spring 2010

Tom, David

The Monte-Carlo Tree Search (MCTS) algorithm Upper Confidence bounds applied to Trees (UCT) has become extremely popular in computer games research. Because of the importance of this family of algorithms, a deeper understanding of when and how their different enhancements work is desirable. To...

Fall 2013

Nakhost, Hootan

This thesis introduces random walk (RW) planning as a new search paradigm for satisficing planning by studying its theory, its practical relevance, and applications. We develop a theoretical framework that explains the strengths and weaknesses of random walks as a tool for heuristic search....

Spring 2014

Van Eyck, Gabriel

Monte Carlo tree search (MCTS) combined with the upper confidence bounds applied to trees (UCT) algorithm has brought forth many advances in game related AI. This includes general game players and programs for specific games such as Amazons, Arimaa, and Go. However, there often is a need for...

Fall 2022

Tapkan, Mustafa B

Imperfect information games model many large-scale real-world problems. Hex is the classic two-player zero-sum no-draw connection game where each player wants to join their two sides. Dark Hex is an imperfect information version of Hex in which each player sees only their own moves. Finding Nash...

Spring 2022

Haque, Rejwana

Board game research has pursued two distinct but linked objectives: solving games, and strong play using heuristics. In our case study in the game of chess, we analyze how current AlphaZero type architectures learn and play late chess endgames, which are solved. We study the open-source program...