• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.40-46
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• 2022

Recently, as the need for data access by integrating information in a distributed cloud environment increases in enterprise-wide enterprises, interoperability for collaboration between existing legacy systems is emphasized. In order to interconnect independent legacy systems, it is necessary to overcome platform heterogeneity and semantic heterogeneity. To solve this problem, middleware was built using EMRA (Extended MetaData Registry Access) based on ISO/IEC 11179. However, the designed middleware cannot guarantee the efficiency of information utilization because it does not have an adjustment function for each node's resource status and work status. Therefore, it is necessary to manage and adjust the legacy system. In this paper, we coordinate the correct data access between the information requesting agent and the information providing agent, and integrate it by designing a cooperative agent responsible for information monitoring and task distribution of each legacy system and resource management of local nodes. to make efficient use of the available information.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.2
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• pp.44-49
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• 2007

An agent is an abstract unit for problem solving in the field of distributed artificial intelligence, and an agent-based system is designed and implemented based on the definition of agent as its central concept. Agent modeling is advantageous to abstraction, disintegration and structuring for describing complex system, so its application is increased in various areas including air traffic control, power transmission, e-commerce and medicine. There is no agreed definition of agent but agents have common points as follows: autonomy, reactivity, pro-activeness and cooperation. An agent-oriented modeling is an approach of a concept different form existing object-oriented modeling. This study proposed the agent application for E-Beam manufacturing system. To evaluate the performance of the proposed process design, we used the JADE library. The JADE toolkit provides a FIPA-compliant agent platform and a package to develp Java agents. It provides a basic set of functionalities that are regarded as essential for an autonomous agent architecture.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.192-198
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• 2022

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

• 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.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.205-210
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• 2022

Drones, which have rapidly grown along with the 4th industrial revolution, spread over industries and also widely used for military purposes. In recent wars in Europe, drones are being evaluated as a game changer on the battlefield, and their importance for military use is being highlighted. The Republic of Korea Army also planned drone-bot systems including various drones suitable for echelons and missions of the military as future defense forces. The keyword of these drone-bot systems is autonomy by artificial intelligence. In addition, common use of operating platforms is required for the rapid development of various types of drones. In this paper, we propose software framework that applies diverse artificial intelligence technologies such as multi-agent system, cognitive architecture and knowledge-based context reasoning for mission autonomy and common use of military drones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

• Advances in robotics research
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• v.2 no.3
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• pp.247-257
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• 2018

Various studies have been performed to coordinate robots in transporting objects and different artificial intelligence algorithms have been considered in this field. In this paper, we investigate and solve Multi-Robot Transportation problem by using a combined auction algorithm. In this algorithm each robot, as an agent, can perform the auction and allocate tasks. This agent tries to clear the auction by studying different states to increase payoff function. The algorithm presented in this paper has been applied to a multi-robot system where robots are responsible for transporting objects. Using this algorithm, robots are able to improve their actions and decisions. To show the excellence of the proposed algorithm, its performance is compared with three heuristic algorithms by statistical simulation approach.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.93.5-93
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• 2002

Distributed Artificial Intelligence (DAI) aims to study and develop techniques that allow interaction among intelligent entities. In the last two decades, some types of DAI architecture have been proposed for various fields. However, it can be noticed that the inconsistency phenomenon has not been dealt with properly. This is probably due to the fact that this phenomenon cannot be handled (at least directly) with classical logic. Hence, to deal with such inconsistencies directly, one should employ a logic other than the classical one. The DAI Architecture described in this work is based on a nonclassical logic called Annotated Paraconsistent Logic.