• Journal of Korea Game Society
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• v.3 no.1
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• pp.74-85
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• 2003

If the game characters in the cyber world speak the same dialogues as in the real world, it will give game players more fun and realism. And game players are more and more immersed into the cyber space. However, we observed that only simple and primitive dialogues are used at the market places in most MMORPGS. We introduce personality psychology theory for generating the personality of NPC in MMORPG. And we suggest how to make a conversation between PC (Playable Character) and NPC (Non - Playable Character) according to 'Extroversion - Introversion dimension' and 'Neuroticism dimension'. And we implement the personality dialogue generation program, which is composed of two parts. One is for generating personality, and the other for making dialogue. Personality generator can set a NPC's personality automatically, and Dialogue Maker can control the dialogue styles, quantities, and characteristics of NPC. Thus, the program implemented in this paper can help game designer and scenario writer to make game characters easily. The approach in this paper can be applied to generate various game characters and used to represent agents and avatars of real-time animation.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.55-61
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• 2020

In this paper, we present a novel approach to efficiently control the location of NPC(Non-playable characters) in the interactive virtual world such as game, virtual reality. To control the NPC's movement path, we first calculate the main trajectory based on the user's path, and then move the NPC based on the weight map. Our method constructs automatically a navigation mesh that provides new paths for NPC by referencing the user trajectories. Our method enables adaptive changes to the virtual world over time and provides user-preferred path weights for smartagent path planning. We have tested the usefulness of our algorithm with several example scenarios from interactive worlds such as video games, virtual reality. In practice, our framework can be applied easily to any type of navigation in an interactive world.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1296-1306
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• 2020

Childhood verbal communication impacts children's language skills and has a positive effect as partners use more vocabulary. But reduction in family time, caused by lowered age for private education and so on, has reduced the chance for children to speak with partners who have a proficient language skill. This vacancy was naturally occupied by the media, which has become one of the cornerstones of the growth of kids' contents. Kids contents are making various attempts to expand the breadth of services. But most contents still focus on unilateral visual information delivery yet, so there is a limit to satisfy the vacancy of conversation partners. Therefore this paper suggests an ECA(Embodied conversational agent) to induce children's spoken conversation using a virtual character frequently used in kids contents. This system is implemented by the voice bot and agent model produced using an IBM assistant and Unity. As a result of using ECA for 66 children of 5-9 years old, it showed meaningful results in terms of induction of verbal communication.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of KIISE:Information Networking
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• v.29 no.3
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• pp.286-298
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• 2002

The Remote Procedure Call(RPC) has been traditionally used for Inter Process Communication(IPC) among precesses in distributed computing environment. As distributed applications have been complicated more and more, the Mobile Agent paradigm for IPC is emerged. Because there are some paradigms for IPC, researches to evaluate and compare the performance of each paradigm are issued recently. But the performance models used in the previous research did not reflect real distributed computing environment correctly, because they did not consider the evacuation elements for providing security services. Since real distributed environment is open, it is very vulnerable to a variety of attacks. In order to execute applications securely in distributed computing environment, security services which protect applications and information against the attacks must be considered. In this paper, we evaluate and compare the performance of the Remote Procedure Call with that of the Mobile Agent in IPC paradigms. We examine security services to execute applications securely, and propose new performance models considering those services. We design performance models, which describe information retrieval system through N database services, using Petri Net. We compare the performance of two paradigms by assigning numerical values to parameters and measuring the execution time of two paradigms. In this paper, the comparison of two performance models with security services for secure communication shows the results that the execution time of the Remote Procedure Call performance model is sharply increased because of many communications with the high cryptography mechanism between hosts, and that the execution time of the Mobile Agent model is gradually increased because the Mobile Agent paradigm can reduce the quantity of the communications between hosts.

• Journal of Korea Game Society
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• v.9 no.3
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• pp.139-150
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• 2009

This paper attempts to investigate the establishment of an interactive knowledge base for action planning by virtual agents and an interactive knowledge-based planning system. A fixed knowledge base is unable to properly handle a change in circumstances because fixed planning is only available under a fixed knowledge base. Therefore, this paper proposes the establishment of an interactive knowledge base which is applicable to diverse environments and an artificial intelligence planning system in which an interactive knowledge base is available. The interactive knowledge base proposed in this paper consists of motivation, behavior, object and action. The association relationship between knowledge base and its input is set using an automation tool. With this tool, a user can easily add to or amend the components of the knowledge base. With this knowledge base, a character plans all action items and chooses one of them to take an action. Since a new action can be applicable by updating the knowledge base even when the character environment changes, it is very useful for virtual reality content developers. This paper has established a relationship between scalable interactive knowledge base components and other components and proposes a convenient input tool and a planning system algorithm effective for an interactive knowledge base. The results of this study have been verified through testing in a virtual environment ('virtual library').