PENERAPAN DECISION TREE DALAM AGEN CERDAS "UNDA ANAK PINTAR" PERMAINAN EDUKASI MUATAN LOKAL BAHASA BANJAR

The educational game "Unda Anak Pintar" is research and development of a puzzle game, it was designed to sharpen memories. In this game, players must read random letters and make words in the Banjar language. Artificial intelligence is also applied in this research. Using the decision tree, game agents can accompany children to play like teachers. The game agent as a virtual teacher can provide sad and happiness emoticon according to the game environment. The results of this study, AI make educational games more interesting and interactive for children.

Implementasi Deteksi Warna Pada Game Finding Color Menggunakan Ekstraksi Fitur Warna dan Fuzzy Decision Tree

Recognizing color is an ability that must be trained from an early age. But the ability to recognize color in early childhood is still low. One of the most preferred media by children is games. Android mobile games have the advantage of providing interactive multimedia, challenges, and rewards. Finding Color games can be used to help children play actively in exploring and observing colors in the surrounding environment because they can detect colors using the camera. The stages in color detection in games are feature extraction and color classification. Color feature extraction will get the color value at the selected pixel coordinates on the screen. The color classification uses fuzzy decision tree ID3. The test results of the application of color feature extraction produce an accuracy value of 100%. The fuzzy decision tree classification is evaluated using a confusion matrix, resulting in an accuracy value in outdoor light conditions of 94.4%. There is an increase of 4.4% compared to indoor conditions. Keywords— Color Detection; Color Feature Extraction; Fuzzy Decision Tree; Game; Mobile Android

The dynamic Nash bargaining solution for 2-stage cost sharing game

The problem of constructing the Dynamic Nash Bargaining Solution in a 2-stage game is studied. In each stage, a minimum cost spanning tree game is played, all players select strategy profiles to construct graphs in the stage game. At the second stage, players may change the graph using strategy profiles with transition probabilities, which decided by players in the first stage. The players' cooperative behavior is considered. As solution the Dynamic Nash Bargaining Solution is proposed. A theorem is proved to allow the Dynamic Nash Bargaining Solution to be time-consistent.

Formative Knowledge Assessment Through Games Using Concept Map and Game Theory

This paper presents the results of a research work carried out in order to develop a novel gaming model for formative knowledge assessment (FKA). The benefits of FKA are well acknowledged by educators as it aids for better learning. As FKA is more frequent, the students’ willingness to take frequent assessments is poor. Often the students expressed that they felt uncomfortable and this resulted in lack of active participation. These are considered as major challenges to be addressed. Hence, this research aimed to find a way to make the assessments interesting. Based on the positive responses for game-based learning, a theoretical model that integrates the principles of extensive games (EGs) and concept map (CM), namely concept tree game (CTG), has been defined and developed. Further, a qualitative research has been performed to evaluate the impact of the FKA model. Major data gathering techniques used in this work were a class test, proposed FKA method, participant observation, survey and focussed team interview. The data were analysed statistically and the results revealed that it has been effective and useful for educators in assessing the knowledge of students and for students, it aided them to know where they stand and helped them to learn effectively.

Computational Aspects of the Preference Cores of Supermodular Two-Scenario Cooperative Games

In a cooperative game, the utility of a coalition of players is given by the characteristic function, and the goal is to find a stable value division of the total utility to the players. In real-world applications, however, multiple scenarios could exist, each of which determines a characteristic function, and which scenario is more important is unknown. To handle such situations, the notion of multi-scenario cooperative games and several solution concepts have been proposed. However, computing the value divisions in those solution concepts is intractable in general. To resolve this issue, we focus on supermodular two-scenario cooperative games in which the number of scenarios is two and the characteristic functions are supermodular and study the computational aspects of a major solution concept called the preference core. First, we show that we can compute the value division in the preference core of a supermodular two-scenario game in polynomial time. Then, we reveal the relations among preference cores with different parameters. Finally, we provide more efficient algorithms for deciding the non-emptiness of the preference core for several specific supermodular two-scenario cooperative games such as the airport game, multicast tree game, and a special case of the generalized induced subgraph game.