• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.37-48
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• 2009

Household service robots should be able to provide their users with a variety of temporal reasoning services. In this paper, we propose an effective way of developing such temporal reasoning services using a domain-independent AI planner. Developing temporal reasoning services with a domain-independent AI planner, we have to address both the knowledge engineering problem of how to represent various real-world temporal constraints in a planning domain definition language, and the system design problem of how to realize the interface between the AI planner and the service consumer. In this paper, we introduce an example scenario and a set of typical temporal constraints for a household service robot, and then present how to represent them in the standard planning domain definition language. We also explain how to implement a service agent based on an AI planner in order to develop and provide new services efficiently.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.9
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• pp.41-48
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• 2019

Recently as the rapid increasing of the elderly, silver housing has grown up. Though much fire evacuation and safety law, fire accident is most dangerous problem in our society. So this study is the purpose to analyze evacuation safety in urban silver housing of floor planning by corridor types using human behavior simulation. The methodology of this study is 'Literature Review' and 'Simulation'. This study has been carried out on silver housing's definition, types, fire safety theory and relation law. To proof evacuation safety, this study measured escaping time, longest distance, and bottleneck counting using human behavior simulation. This study use game engine simulation program to analyze 6 corridor types experimental model. As the result of simulation, this study compare between ASET and simulation result. The result come down to 3 part. First, double loaded corridor type is the most dangerous on urban silver housing. Second, Safe shelter's location and number cause increasing of escaping time. Lastly escaping time is influenced by behavior of agent, bottle neck strike frequency.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7757-7759
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• 2013

Physicians, in order to study the causes of cancer, detect cancer earlier, prevent or determine the effectiveness of treatment, and specify the reasons for the treatment ineffectiveness, need to access accurate, comprehensive, and timely cancer data. The cancer care environment has become more complex because of the need for coordination and communication among health care professionals with different skills in a variety of roles and the existence of large amounts of data with various formats. The goals of health care systems in such a complex environment are correct health data management, providing appropriate information needs of users to enhance the integrity and quality of health care, timely access to accurate information and reducing medical errors. These roles in new systems with use of agents efficiently perform well. Because of the potential capability of agent systems to solve complex and dynamic health problems, health care system, in order to gain full advantage of E- health, steps must be taken to make use of this technology. Multi-agent systems have effective roles in health service quality improvement especially in telemedicine, emergency situations and management of chronic diseases such as cancer. In the design and implementation of agent based systems, planning items such as information confidentiality and privacy, architecture, communication standards, ethical and legal aspects, identification opportunities and barriers should be considered. It should be noted that usage of agent systems only with a technical view is associated with many problems such as lack of user acceptance. The aim of this commentary is to survey applications, opportunities and barriers of this new artificial intelligence tool for cancer care information as an approach to improve cancer care management.

• Journal of Korea Game Society
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• v.6 no.3
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• pp.13-22
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• 2006

Games such as sports, RTS, RPG, which teams of players play, require advanced artificial intelligence technology for team management. The existing artificial intelligence enables an intelligent agent to have the autonomy solving problem by itself, but to lack interaction and cooperation between agents. This paper presents "Level Unified Approach Method" with effective role allocation and autonomy in multiagent system. This method allots sub-goals to agents using role information to accomplish a global goal. Each agent makes a decision and takes actions by itself in dynamic environments. Global goal of Team coordinates to allocated role in tactics approach. Each agent leads interactive cooperation by sharing state information with another using Databoard, As each agent has planning capacity, an agent takes appropriate actions for playing allocated roles in dynamic environments. This cooperation and interactive operation between agents causes a collision problem, so it approaches at tactics side for controlling this problem. Our experimental result shows that "Level Unified Approach Method" has better performance than existing rental approach method or de-centralized approach method.

• Journal of Digital Contents Society
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• v.11 no.4
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• pp.607-614
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• 2010

History centered knowledge based story-telling project planning tool supports the process of story creation in narrative genre about history of families or individuals. Narrative fields not only include drama, mythology, legend, history but also non-verbal epics such as movie, play, ballet and opera. But as verbal epic, this research paper focuses on the family history and individual history of each household. This story-telling planning tool redevelops each genre of story-telling about family history through sampleDB and informationDB, and it is widely applicable in concreting high quality stories in both its content and value. Reduces the time of planning story-telling, and impose minimum expenses in human resources. Content about family history is one of the most the fundamental and renowned contents in Story-telling but planning tool that is easily applicable in creating such content does not exist in statue quo. In this current system lacking creative infra, this research paper seeks to provide a planning tool that public can easily utilize, and by systemizing the tool. it aims to create a creative contents tool model applicable in variety of genres.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.15-20
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• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.161.2-161.2
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• 2015

Melanin is a black pigment, responsible for hair and skin color. In order to find the melanin stimulatory technique which prove useful for a gray and a white hair-preventive agent or tanning agent, we developed atmospheric pressure plasma jet (APPJ) and tested for tyrosinase activity and melanin production in melanoma (B16F10) cells in vitro. We found plasma dose dependent increase in melanin production. To explore the contributing mechanism in melanin synthesis, intracellular reactive oxygen species (ROS) and MAP kinase signaling pathways were studied. Furthermore, the development of plasma technology for melanin synthesis and planning for in-vivo future studies will be discussed.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.729-736
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• 2004

Over the last few years knowledge management has become more and more impotant part of the in healthcare practices. Therefore the healthcare organisatiions have also begun to apply knowlege management strategies. To address this issue of the lack of true knowledge management in healthcare enterprises, we propose a framework for common Healthcare Knowledge Management. This framework is made up of two suites of applications and services, i.e. the intelligent agent-based knowledge management application suite and the strategic visualisation, planning and coalition formation service suite.