• Journal of Korea Game Society
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• v.17 no.4
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• pp.101-108
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• 2017

This study explores how Artificial Intelligence (AI), which is tremendously developed these days, applies to the game and advances. It analyzes the reality of AI and provides reasonable suggestion in Poker, one of the most popular games. Specifically, this study focuses on Texas Hold'em, the most favored kind in the world among various kinds of Poker games and deals with two AIs, Libratus and DeepStack that have applied to the game. Several news media report the growth of AI, but this study will multi-dimensionally discusses how and why AI works in Poker, the real problems of AI, and suggestions for advancement.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.253-258
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• 2008

Recently, game players want new game requiring more various tactics and strategies in the complex environment beyond simple and repetitive play. Various artificial intelligence techniques have been suggested to make the game characters learn within this environment, and the recent researches include the neural network and the genetic algorithm. The Genetic programming(GP) has been used in this study for learning strategy of the agent in the pursuit-evasion problem which is used widely in the game theories. The suggested GP algorithm is faster than the existing algorithm such as neural network, it can be understood instinctively, and it has high adaptability since the evolving chromosomes can be transformed to the reasoning rules.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.106-117
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• 2022

In 2021, AI basics were introduced in the high school curriculum. There are many worries that the problem of utilization-oriented education will be repeated with the introduction of artificial intelligence education rather than the principles that occurred when ICT was applied to education in the past. Most of the existing AI education platforms focus only on the use of AI. For artificial intelligence education of middle school students, there are difficulties in learning about the process by which artificial intelligence derives results and learning the principles of artificial intelligence algorithms. Recently, as the educational application of metaverse has become a hot topic, research has been started to improve learning achievement by arousing students' immersion and interest. This research developed educational game contents about decision tree algorithm using metaverse as educational contents that can be used in middle school AI education. By applying games to education, it was intended to increase students' interest and immersion in artificial intelligence, and to increase educational effectiveness. In this paper, the educational effectiveness, difficulty, and level of interest were analyzed for pre-service teachers regarding the developed game content. Based on this, a future principle-oriented artificial intelligence education method was suggested.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.363-368
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• 2005

Artificial life is a science studying artificial systems that implement various behavioral characteristics of lives as an attempt of applying some features found in living creatures to artificial intelligent objects in virtual worlds. Attempts and researches are actively being made to apply human needs to games and express them through artificial life. Human needs and the expression of the needs are extremely diverse and complicated, so they cannot be modeled in a specific way. Thus this study modeled game AI object needs using AHP, which is a useful model in solving problems quantitatively through basic observation of human nature, analytic thinking, measuring, etc. In addition, the modeled game AI object needs were examined through the analysis of performance sensitivity and their applicability to actual games was assessed with example.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.217-223
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• 2011

AI(Artificial Intelligence) refers to the area of computer engineering and IT technology that focuses on the methodology and creation of intelligent agents. The Othello game is often produced with AI, since it is played with relatively simple rules on a board and on a limited space of 8 rows and 8 columns. Previous algorithms take longer time than desirable and often fail to face new circumstances, as they search for all the possible cases and rules. In order to solve this crucial weakness, we propose that a CBR algorithm be applied to Orthello. Case-Based Reasoning(CBR), is the process of solving new problems based on the solutions of the past similar problems. We can apply this process to Othello and expedite the process of computer reasoning for a solution to new cases based on the data from accumulated past cases. Then, these new solutions are dynamically added to the set of past cases so that it becomes harder for players(users) to be able to read the pattern. The proposed system in which a CBR algorithm is applied to the Othello game makes the computation process faster and the game harder to play.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.43-51
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• 2017

As the game industry is blooming, the generation of contents is far behind the consumption of contents. With this reason, it is necessary to afford the game contents considering level of game player's skill. In order to effectively solve this problem, Procedural Content Generation(PCG) using Artificial Intelligence(AI) is one of the plausible options. This paper proposes the procedural method to generate various monsters considering level of player's skill using genetic algorithm. One gene consists of the properties of a monster and one genome consists of genes for various monsters. A generated monster is evaluated by battle simulation with a player and then goes through selection and crossover steps. Using our proposed scheme, players adaptive monsters are generated procedurally based on genetic algorithm and the variety of monsters which are generated with different number of genome is compared.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.89-98
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• 2015

The artificial intelligence (AI) of Non-Player Companions (NPC), especially opponents, is a key element to adjust the level of games in game design. Smart opponents can make games more challenging as well as allow players for diverse experiences, even in the same game environment. Since game users interact with more than one opponent in most of today's games, collaboration control of opponent characters becomes more important than ever before. In this paper, we introduce a cooperative strategy based on the A* algorithm for enemies' AI in the Pac-Man game. A survey from 17 human testers shows that the levels with our collaborative opponents are more difficult but interesting than those with either the original Pac-Man's personalities or the non-cooperative greedy opponents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1181-1187
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• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.

• Journal of Korea Game Society
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• v.14 no.5
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• pp.127-136
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• 2014

NPC in game is an important factor to increase the fun of the game by cooperating with player or confrontation with player. NPC's behavior patterns in the previous games are limited. Also, there is not much difference in NPC's ability among the existing games because it's designed to FSM. Therefore, players who have matched with NPCs which have the characteristics may have difficulty to play. This paper is for improving the problem and production and evaluation of the game NPC behavior model based on wolves hunting model in real life. To achieve it, first, the research surveys and studies behavior states for wolves to capture prey in the real world. Secondly, it is implemented using the Unity3D engine. Third, this paper compares the implemented state transition probability to state transition probability in real world, state transition probability in general game. The comparison shows that the number of state transitions of NPCs increases, proportions of implemented NPC behavior patterns converges to probabilities of state transition in real-world. This means that the aggressive behavior pattern of NPC implemented is similar to the wolf hunting behavior pattern of the real world, and it can thereby provide more player experience.

• Journal of Korea Game Society
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• v.21 no.5
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• pp.17-28
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• 2021

Reinforcement learning, in which artificial intelligence develops itself to find the best solution to problems, is a technology that is highly valuable in many fields. In particular, the game field has the advantage of providing a virtual environment for problem-solving to reinforcement learning artificial intelligence, and reinforcement learning agents solve problems about their environment by identifying information about their situation and environment using observations. In this experiment, the instant dungeon environment of the RPG game was simplified and produced and various observation variables related to the field of view were set to the agent. As a result of the experiment, it was possible to figure out how much each set variable affects the learning speed, and these results can be referred to in the study of game RPG reinforcement learning.