Solve the Problem of Chess Board in the Shape of the Letter L with Three different Patterns Using Artificial Intelligence & Algorithms

In recent years, the chess game has begun to develop successful programming solutions. Computers were programmed to play chess in the middle of the twentieth century. Computer skills have become better and higher than the skills of chess players in the world, and from here this study has made it possible to find the optimal solution for the four square pieces in the form of a letter (L) without repetition and quick access to fill the sites and voids and to complete the entire area. It is our task to cover a (2n×2n) Chessboard with L-shaped tiles each tile is a (2×2) square with a (1×1) square removed from one corner. We are working to cover the Chessboard in such a way that there is a single 1×1 box left in the ‘corner’ of the Chessboard (by the 'corner' we mean one corner of the box should be uncovered). In this task, we will solve this problem with three approaches, the C programming approach, the second by dividing and conquering approach and the last by a greedy method approach. Three algorithms were used and a comparison was made between them, and the fastest method was achieved by a greedy method, with eight cases comparing one and four cases, respectively.

DESIGN OF WEB-BASED UYU STORE ATTENDANCE INFORMATION SYSTEM USING COLORING METHOD

Attendance in the world of work is sometimes still done manually. At the Uyu shop, there are still problems that occur in recording and making attendance reports manually, such as mistakes in biodata, forgetting to record the date. Because the Uyu store still doesn't use a computerized employee or staff absence information system in managing data, so the information that can be processed is quite long and storage is not guaranteed safe. The purpose of this researcher is to create an information system that can manage attendance data for employees who work at the computerized Uyu Store and also implement a coloring method on attendance reports that generate reports based on the attendance coloring method according to employee attendance hours that are easy to understand. This method uses the coloring method as a solution to solving problems that can be solved in the greedy method, namely the color problem and the waterfall model as a system development process that uses UML design. The result of this research is that the system can operate attendance data collection as well as report employee data more efficiently and integrated.

Comparison of various algorithms based on TSP solving

The traveling salesman problem (TSP) is a classic combinatorial optimization problem. As a hot research problem, it has been applied in many fields. Since there is currently no algorithm with good performance that can perfectly solve this problem, for the solution of the traveling salesman problem, this article first establishes a mathematical model of the traveling salesman problem, and uses four solving methods for the same case: the greedy method, Branch and bound method, dynamic programming method and genetic algorithm, analyze the applicability and accuracy of different algorithms in the same case.

An Improved Nesting Algorithm for Irregular Patterns

In industrial environments, nesting consists in cutting or extracting pieces from a material sheet, with the purpose of minimizing the surface of the sheet used. This problem is present in different types of industries, such as shipping, aeronautics, woodworking, footwear, and so on. In this work, the aim is to find an acceptable solution to solve complex nesting problems. The research developed is oriented to sacrifice accuracy for speed so as to obtain robust solutions in less computational time. To achieve this, a greedy method and a genetic algorithm have been implemented, being the latter responsible for generating a sequence for the placement of the pieces, where each piece is placed in its current optimal position with the help of a representation system for both the pieces and the material sheet.

Forced ε-Greedy, an Expansion to the ε-Greedy Action Selection Method

Reinforcement Learning methods such as Q Learning, make use of action selection methods, in order to train an agent to perform a task. As the complexity of the task grows, so does the time required to train the agent. In this paper Q Learning is applied onto the board game Dominion, and Forced ε-greedy, an expansion to the ε-greedy action selection method is introduced. As shown in this paper the Forced ε-greedy method achieves to accelerate the training process and optimize its results, especially as the complexity of the task grows.