http://inpressco.com/wp-content/uploads/2013/08/Paper591099-1103.pdf

Application of graph theory to find optimal paths for the transportation problem

An educational game to teach numbers in Brazilian Sign Language while having fun

Artificial Intelligence curriculum comprises some difficult concepts for a student to grasp and traditional teaching methods are proven to be ineffective and unable to motivate them. A basic AI concept is Constraint Satisfaction (CS), which is acknowledged by most tutors to be a hard domain for teachers to teach and student to understand. To solve a CS problem (CSP), a CS algorithm should be used. In this paper we present a game based approach, to be used as an assistant tool in an AI course, to assist students in understanding CS algorithms in an easy, interactive and motivating way. The game is based on a classical CSP, the map-coloring problem. Preliminary evaluation results are quite encouraging.

Using game-based learning in teaching CS algorithms

Vector quantization (VQ) is a well-known compression method. In the encoding phase, given a block represented as a vector, searching the closest codeword in the codebook is a time-consuming task. In this article, two fast encoding algorithms for VQ are proposed. To reduce the search area and accelerate the search process, the first algorithm utilizes three significant features of a vector that are, the norm, and two projection angles to two projection axes. The second algorithm uses the first two features as in the first algorithm with the projection value of the vector to the second projection axe. The algorithms allow significant acceleration in the encoding process. Experimental results are presented on image block data. These results confirm the effectiveness of the proposed algorithms. [Article copies are available for purchase from InfoSci-on-Demand.com]

Fast Vector Quantization Encoding Algorithms for Image Compression

Search algorithms constitute an important topic in the Artificial Intelligence curriculum and are acknowledged by most tutors to be a hard and complex domain for teachers to teach and students to deeply understand. In this paper, we present an educational computer game, designed to teach search algorithms, based on the popular Pacman game. The purpose of the educational Pacman game is to assist students to understand the artificial intelligence topic of search algorithms in an entertaining, interactive and motivating way. During their experience with the game, students can examine the behaviour of various search algorithms and a graphical annotated depiction of them through suitable visualizations. Visualizations can demonstrate the operational functionality of algorithms and are designed in line with the principles of studentâs active learning. Various learning activities were designed and request students to apply specific search algorithms in various example cases with or without the assistance and feedback of the game. An evaluation study was conducted in real classroom conditions and revealed quite satisfactory results. The results indicate that the educational Pacman game is an effective way to enhance studentsâ engagement and help them to deeper understand the AI search algorithms.

An Educational Game for Teaching Search Algorithms

This paper describes the reasons about why it is beneficial to combine with graph theory and board game. Forbye, it also descants three graph theories: Dijkstra's, Prim's, and Kruskal's minimum spanning tree. Then it would describe the information about the board game we choose and how to combine the game with before-mentioned three graph theories. At last, we would account for the advantage of combining with these three graph theories and the game specifically.

Learning Kruskal's Algorithm, Prim's Algorithm and Dijkstra's Algorithm by Board Game

The concept of minimum spanning tree algorithms in data structure is difficult for students to learn and to imagine without practice. Usually, learners need to diagram the spanning trees with pen to realize how the minimum spanning tree algorithm works. In this paper, the authors introduce a competitive board game to motivate students to learn the concept of minimum spanning tree algorithms. They discuss the reasons why it is beneficial to combine graph theories and board game for the Dijkstra and Prim minimum spanning tree theories. In the experimental results, this paper demonstrates the board game and examines the learning feedback for the mentioned two graph theories. Advantages summarizing the benefits of combining the graph theories with board game are discussed.

Yan-Da Chiu

Papers

Learning Kruskal's Algorithm, Prim's Algorithm and Dijkstra's Algorithm by Board Game

Board Game Supporting Learning Prim's Algorithm and Dijkstra's Algorithm