• Journal of The Korean Association of Information Education
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• v.2 no.2
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• pp.201-208
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• 1998

Recently software complexity will continue to increase dramatically in the coming decades because of the dynamic and distributed nature of both data and applications. Not only must software systems assist in coordinating tasks among humans, they must also help mannage cooperation among distributed programs. Software agent may properly respond to these requirements. This paper investigates the adaptive characteristics found in adaptive tutoring agents. To funcion effectively throughout the range of situations presented by such a dynamic and extremely-distributed information entities, an agent must be highly adaptive. Adaptivity exists in several key aspects of agent's behavior to its dynamic situation. My goal is to show the various aspects of internal adaptivity that could be applied to build comprehensive agents that function effectively in tutoring agent system.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.393-398
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• 2016

Collective Adaptive System is an adaptive multi-agent system which accomplishes its goal by collaborating various agents. Because the collective property of the Collective Adaptive System is accomplished by the goal of the system being based on collaboration, testing the goal accomplishment and their interactions among heterogeneous agents is important. This paper presents a feasibility study of applying a model-based testing approach using task-based test model to a Collective Adaptive System. This paper describes additional information to be applied for Collective Adaptive System for future studies. To analyze our approach, we applied the proposed approach to a smart home system as a case study; our results indicated that we can systematically derive test cases to check whether the Collective Adaptive System successfully achieved its goals by modifying and extending the existing task model.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.94-96
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.91-93
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• 2009

In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of In this paper, we present the Q-learning method for adaptive traffic signal control on the basis of multi-agent technology. The structure is composed of sixphase agents and one intersection agent. Wireless communication network provides the possibility of the cooperation of agents. As one kind of reinforcement learning, Q-learning is adopted as the algorithm of the control mechanism, which can acquire optical control strategies from delayed reward; furthermore, we adopt dynamic learning method instead of static method, which is more practical. Simulation result indicates that it is more effective than traditional signal system.

• Clinical and Experimental Pediatrics
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• v.52 no.6
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• pp.649-654
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• 2009

The immune system is comprised of cells and molecules whose collective and coordinated response to the introduction of foreign substance is referred to as the immune response. Defense against microbes is mediated by the early reaction (innate immunity) and the late response (adaptive immunity). Innate immunity consists of the epithelial barrier, phagocytes, complement and natural killer cells. Adaptive immunity, a more complex defense reaction, consists of activation of later-developed lymphocytes that, when stimulated by exposure to infectious agents, increase in magnitude and defensive capabilities with each successive exposure. In this review we discuss recent advances in important primary immune deficiency disorders of innate immunity (chronic granulomatous disease, leukocyte adhesion deficiency) and adaptive immunity (severe combined immune deficiency, Wiskott- Aldrich syndrome).

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.148-164
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• 2015

The use of mobile agents for collaborative processing in wireless sensor network has gained considerable attention. This is when mobile agents are used for data aggregation to exploit redundant and correlated data. The efficiency of agent-based data aggregation depends on the agent migration scheme. However, in general, most of the proposed schemes are centralized approach-based schemes where the sink node determines the migration paths for the agents before dispatching them in the sensor network. The main limitations with such schemes are that they need global network topology information for deriving the migration paths of the agents, which incurs additional communication overhead, since each node has a very limited communication range. In addition, a centralized approach does not provide fault tolerant and adaptive migration paths. In order to solve such problems, we have proposed a distributed approach-based scheme for determining the migration path of the agents where at each hop, the local information is used to decide the migration of the agents. In addition, we also propose a local repair mechanism for dealing with the faulty nodes. The simulation results show that the proposed scheme performs better than existing schemes in the presence of faulty nodes within the networks, and manages to report the aggregated data to the sink faster.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.795-802
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• 2003

To live autonomously, intelligent agents such as robots or virtual characters need ability that recognizes given environment, and learns and chooses adaptive actions. So, we propose an action selection/learning mechanism in intelligent agents. The proposed mechanism employs a hybrid system which integrates a behavior-based method using the reinforcement learning and a cognitive-based method using the symbolic learning. The characteristics of our mechanism are as follows. First, because it learns adaptive actions about environment using reinforcement learning, our agents have flexibility about environmental changes. Second, because it learns environmental factors for the agent's goals using inductive machine learning and association rules, the agent learns and selects appropriate actions faster in given surrounding and more efficiently in extended surroundings. Third, in implementing the intelligent agents, we considers only the recognized states which are found by a state detector rather than by all states. Because this method consider only necessary states, we can reduce the space of memory. And because it represents and processes new states dynamically, we can cope with the change of environment spontaneously.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.165-175
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• 2007

The mobile agent executes a given task by which the agent code moves to the server directly. Therefore, node migration method becomes an important factor which impact on the whole performance of distributed system. In this paper, we propose an adaptive migration path technique of mobile agent using the metadata of naming agent. In this proposed technique, node selection for migration depends on the content of referenced metadata, and the reliability of migrated information is determined by the metadata updating method and cooperative operations of individual agents in multi-agents system. For these, we design the metadata using by the number of hit documents, hit ratio, node processing time and network delay time, and describe the methods for creating, using and updating metadata for which determine the adaptive node migration path of mobile agent according to the cooperation of individual agents and number of hit documents using by designed metadata. And results of evaluated performance for proposed adaptive migration path technique through the proper experiment and analysis gain rate of high effective information earning, because of high hit ratio(72%) about of fathered documents by case of applying metadata move to the 13 nodes. But, in case of non-applying metadata is hit ratio(46%) of gathered documents and rate of effective information earning about of 26 nodes is 36.8%.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.118-118
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• 1999

In this paper, we propose an agent negotiation algorithm of an intelligent agent system for an active and adaptive multimedia data services in the distributed environment. We describe theEARTS-II system that performs automatic job seeking and job offering operations using intelligentagents without user's intervention. EARTS-II offers best candidate lists as the results to usersthrough negotiation among agents considering conditions given by the users. And according to theresults, the EARTS-II supports real processes of employment, The negotiation algorithm tries tosatisfy all agents in the job market. To test the performance of the algorithm, simulation results arepresented.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.141-147
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• 2009

To initialize and maintain self-organizing networks such as mobile ad hoc networks, address allocation protocol is essentially required. However, centralized approaches that pervasively used in traditional networks are not recommended in this kind of networks since they cannot handle with mobility efficiently. In addition, previous distributed approaches suffer from inefficiency with control overhead caused by duplicated address detection and management of available address pool. In this paper, we propose a new dynamic address allocation scheme, which is based on adaptive partition. An available address is managed in distributed way by multiple agents and partitioned adaptively according to current network environments. Finally, simulation results reveal that a proposed scheme is superior to previous approach in term of address acquisition delay under diverse simulation scenarios.