Monte Carlo tree search (MCTS) is a recently proposed search method that combines the
precision of tree search with the generality of random sampling. It has received considerable …

This thesis studies the use of Monte-Carlo simulations for tree-search problems. The Monte-
Carlo technique we investigate is Monte-Carlo Tree Search (MCTS). It is a best-first search …

Classic methods such as A∗ and IDA∗ are a popular and successful choice for one-player
games. However, without an accurate admissible evaluation function, they fail. In this article …

High-Resolution Range Profile (HRRP) sequence has attracted academic attentions in the
field of radar automatic target recognition (RATR) owing to its abundant spatial-temporal …

Turn-based multi-action adversarial games are games in which each player turn consists of
a sequence of atomic actions, resulting in an extremely high branching factor. Many strategy …

Simulation balancing is a new technique to tune parameters of a playout policy for a Monte-
Carlo game-playing program. So far, this algorithm had only been tested in a very artificial …

Optimal simultaneous coalition structure generation and assignment is computationally
hard. The state-of-the-art can only compute solutions to problems with severely limited input …

Many enhancements for Monte Carlo tree search (MCTS) have been applied successfully in
general game playing (GGP). MCTS and its enhancements are controlled by multiple …

Artificial intelligence in games often leads to the problem of parameter tuning. Some
heuristics may have coefficients, and they should be tuned to maximize the win rate of the …

The utilization of high-risk test cases constitutes an effective approach to enhance the safety
testing of autonomous vehicles (AVs) and enhance their efficiency. This research paper …