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In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game "Hearthstone: Heroes of Warcraft". We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible...
General Video Game Playing (GVGP) algorithms are usually focused on winning and maximizing score but combining different objectives could turn out to be a solution that has not been deeply investigated yet. This paper presents the results obtained when five GVGP agents play a set of games using heuristics with different objectives: maximizing winning, maximizing exploration, maximizing the discovery...
Inspired by the core idea of AlphaGo, we combine a neural network, which is trained by Adaptive Dynamic Programming (ADP), with Monte Carlo Tree Search (MCTS) algorithm for Gomoku. MCTS algorithm is based on Monte Carlo simulation method, which goes through lots of simulations and generates a game search tree. We rollout it and search the outcomes of the leaf nodes in the tree. As a result, we obtain...
The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently whereas in real world applications, those constraints are handled concurrently. This paper describes a novel approach to solve variants of Open VRP, Multi Depot VRP, Capacitated...
General video game playing is a challenging research area in which the goal is to find one algorithm that can play many games successfully. “Monte Carlo Tree Search” (MCTS) is a popular algorithm that has often been used for this purpose. It incrementally builds a search tree based on observed states after applying actions. However, the MCTS algorithm always plans over actions and does not incorporate...
The game of NoGo needs to be optimized its theoretical system and evaluation methods since it is been proposed in recent years. At present, most of the programs of NoGo are based on Monte-Carlo Tree Search (MCTS), but its programs are rarely based on static evaluation algorithm. In this paper, according to the rules and characteristics of NoGo, a static evaluation method has been put forward. In the...
There are a number of Artificial Intelligence (AI) algorithms for implementation of “Blokus Duo” game. We needed an implementation on FPGA, and moreover, the design had to respond under a given time constraint. In this paper we examine some of these algorithms and propose a heuristic algorithm to solve the problem by considering intelligence, time constraint and FPGA implementation limitations.
In this paper, we present a Monte-Carlo policy rollout technique (called MOCART-CGA) for path planning in dynamic and partially observable real-time environments such as Real-time Strategy games. The emphasis is put on fast action selection motivating the use of Monte-Carlo techniques in MOCART-CGA. Exploration of the space is guided by using corridors which direct simulations in the neighbourhood...
Internal states in a computer GO program are typically organized as a game tree. While simple and convenient, the game tree organization may result in more tree nodes than actual internal states because duplicated tree nodes exist to represent the same gaming state. Modern computer GO programs use the UCT algorithm to conduct game tree search. Duplicated nodes in the game tree representation would...
This paper proposes two dynamic randomization techniques for Monte-Carlo Go that uses Monte-Carlo tree search with UCT algorithm. First, during the in-tree phase of a simulation game, the parameters are randomized in selected ranges before each simulation move. Second, during the playout phase, the order of simulation move generators are hierarchically randomized before each playout move. Both dynamic...
The researches on Go using Monte Carlo method are treated as hot topics in these years. In particular, Monte Carlo Tree Search algorithm such as UCT made great contributions to the development of computer Go. When Monte Carlo method was used for Go, the previous simulation results were not usually stored. In this paper, we suggest a new idea of using previous simulation results (PSR heuristic) and...
Protein prediction is a fundamental problem in biology the most application of solving this problem is drug design where we search for a special shape to interaction of protein. In this paper we want to solve this problem in 2D environment as same as method of navigation for robot. In others word, this problem is similar to know the best optimal structure of path of a robot which meeting a predetermined...
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