The article presents the path planning algorithm to be applied in the Chinese chess game, and uses multiple mobile robots to present the experimental scenario. Users play the Chinese chess game using the mouse on the ...The article presents the path planning algorithm to be applied in the Chinese chess game, and uses multiple mobile robots to present the experimental scenario. Users play the Chinese chess game using the mouse on the supervised computer. The supervised computer programs the motion paths using A＊ searching algorithm, and controls mobile robots moving on the grid based chessboard platform via wireless radio frequency （RF） interface. The A＊ searching algorithm solves shortest path problems of mobile robots from the start point to the target point, and avoids the obstacles on the chessboard platform. The supervised computer calculates the total time to play the game, and computes the residual time to play chess in the step for each player. The simulation results can fired out the shortest motion paths of the mobile robots （chesses） moving to target points from start points in the monitor, and decides the motion path to be existence or not. The eaten chess can moves to the assigned position, and uses the A＊ searching algorithm to program the motion path, too. Finally, the authors implement the simulation results on the chessboard platform using mobile robots. Users can play the Chinese chess game on the supervised computer according to the Chinese chess game rule, and play each step of the game in the assigned time. The supervised computer can suggests which player don＇t obey the rules of the game, and decides which player to be a winner. The scenario of the Chinese chess game feedback to the user interface using the image system.展开更多
Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans.The CaseBased Reasoning(CBR)strategy tries to simulate human thinking regarding solvi...Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans.The CaseBased Reasoning(CBR)strategy tries to simulate human thinking regarding solving problems based on constructed knowledge.This paper suggests a new Action-Based Reasoning(ABR)strategy for a chess engine.This strategy mimics human experts’approaches when playing chess,with the help of the CBR phases.This proposed engine consists of the following processes.Firstly,an action library compiled by parsing many grandmasters’cases with their actions from different games is built.Secondly,this library reduces the search space by using two filtration steps based on the defined action-based and encoding-based similarity schemes.Thirdly,the minimax search tree is fed with a list extracted from the filtering stage using the alpha-beta algorithm to prune the search.The proposed evaluation function estimates the retrievably reactive moves.Finally,the best move will be selected,played on the board,and stored in the action library for future use.Many experiments were conducted to evaluate the performance of the proposed engine.Moreover,the engine played 200 games against Rybka 2.3.2a scoring 2500,2300,2100,and 1900 rating points.Moreover,they used the Bayeselo tool to estimate these rating points of the engine.The results illustrated that the proposed approach achieved high rating points,reaching as high as 2483 points.展开更多
文摘The article presents the path planning algorithm to be applied in the Chinese chess game, and uses multiple mobile robots to present the experimental scenario. Users play the Chinese chess game using the mouse on the supervised computer. The supervised computer programs the motion paths using A＊ searching algorithm, and controls mobile robots moving on the grid based chessboard platform via wireless radio frequency （RF） interface. The A＊ searching algorithm solves shortest path problems of mobile robots from the start point to the target point, and avoids the obstacles on the chessboard platform. The supervised computer calculates the total time to play the game, and computes the residual time to play chess in the step for each player. The simulation results can fired out the shortest motion paths of the mobile robots （chesses） moving to target points from start points in the monitor, and decides the motion path to be existence or not. The eaten chess can moves to the assigned position, and uses the A＊ searching algorithm to program the motion path, too. Finally, the authors implement the simulation results on the chessboard platform using mobile robots. Users can play the Chinese chess game on the supervised computer according to the Chinese chess game rule, and play each step of the game in the assigned time. The supervised computer can suggests which player don＇t obey the rules of the game, and decides which player to be a winner. The scenario of the Chinese chess game feedback to the user interface using the image system.
文摘Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans.The CaseBased Reasoning(CBR)strategy tries to simulate human thinking regarding solving problems based on constructed knowledge.This paper suggests a new Action-Based Reasoning(ABR)strategy for a chess engine.This strategy mimics human experts’approaches when playing chess,with the help of the CBR phases.This proposed engine consists of the following processes.Firstly,an action library compiled by parsing many grandmasters’cases with their actions from different games is built.Secondly,this library reduces the search space by using two filtration steps based on the defined action-based and encoding-based similarity schemes.Thirdly,the minimax search tree is fed with a list extracted from the filtering stage using the alpha-beta algorithm to prune the search.The proposed evaluation function estimates the retrievably reactive moves.Finally,the best move will be selected,played on the board,and stored in the action library for future use.Many experiments were conducted to evaluate the performance of the proposed engine.Moreover,the engine played 200 games against Rybka 2.3.2a scoring 2500,2300,2100,and 1900 rating points.Moreover,they used the Bayeselo tool to estimate these rating points of the engine.The results illustrated that the proposed approach achieved high rating points,reaching as high as 2483 points.
