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A Practical Solution to Handling Randomness and Imperfect Information in Monte Carlo Tree Search

Świechowski Maciej, Tajmajer Tomasz

Annals of Computer Science and Information Systems

2021

Vol. 25

101--110

This paper provides practical guidelines for developing strong AI agents based on the Monte Carlo Tree Search algorithm in a game with imperfect information and/or randomness. These guidelines are backed up by series of experiments carried out in the very popular game - Hearthstone. Despite the focus on Hearthstone, the paper is written with reusability and universal applications in mind. For MCTS algorithm, we introduced a few novel ideas such as complete elimination of the so-called nature moves, separation of decision and simulation states as well as a multi-layered transposition table. These have helped to create a strong Hearthstone agent.

Heuristics for Thelen's Prime Implicant Method

Bieganowski J., Karatkevich A.

Schedae Informaticae

2005

Vol. 14

125-135

Thelen's algorithm is an efficient method for generation of the prime implicants of a Boolean function represented in CNF. In the paper new heuristics are presented, allowing to accelerate the algorithm. Experimental analysis of their effects is performed.