• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.12
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• pp.71-82
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• 2003

In this paper, we present a new method to calculate reinforcement value in QoS routing algorithm targeted for real-time applications based on Ant algorithm to efficiently and effectively reinforce ant-like mobile agents to find the best route toward destination in a network regarding necessary QoS metrics. Simulation results show that the proposed method realizes QoS routing more efficiently and more adaptively than those of the existing method thereby providing better solutions for the best route selection for real-time application that has high priority on delay jitter and bandwidth.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.137-147
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• 2007

As Web-based online communities are rapidly growing, the agents in social groups need to know their measurable belief of trust for safe andsuccessful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The averagetrust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. And then, we precisely describe the relationship between reputation, trust, and averagetrust through a concrete example of their computations. We apply our trust model to online internet settings in order to show how trust mechanisms are involved in a rational decision-making of the agents.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.101-110
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• 2004

In dynamic systems, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to Changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results are presented to demonstrate that co-evolutionary learning with coalitions and confidence allows better performing strategies that generalize well.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.5 s.335
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• pp.29-38
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• 2005

In this paper, a new modified and optimized AntNet algorithm which can be implemented efficiently onto network processor is proposed. The AntNet that mimics the activities of the social insect is an adaptive agent-based routing algorithm. This method requires a complex arithmetic calculating system. However, since network processors have simple arithmetic units for a packet processing, it is very difficult to implement the original AntNet algorithm on network processors. Therefore, the proposed AntNet algorithm is a solution of this problem by decreasing arithmetic executing cycles for calculating a reinforcement value without loss of the adaptive performance. The results of the simulations show that the proposed algorithm is more suitable and efficient than the original AntNet algorithm for commercial network processors.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• Radiation Oncology Journal
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• v.12 no.1
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• pp.1-8
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• 1994

When cells are exposed to low doses of a mutagenic or clastogenic agents. they often become less sensitive to the effects of a higher dose administered subsequently. Such adaptive responses were first described in Escherichia coli and mammalian cells to low doses of an alkylating agent. Since most of the studies have been carried out with human lymphocytes, it is urgently necessary to study this effect in different cellular systems. Its relation with inherent cellular radiosensitivity and underlying mechanism also remain to be answered. In this study, adaptive response by 1 cGy of gamma rays was investigated in three human lymphoblastoid cell lines which were derived from ataxia telangiectasia homozygote, ataxia telangiectasia heterozygote, and normal individual. Experiments were carried out by delivering 1 cGy followed by 50 cGy of gamma radiation and chromatid breaks were scored as an endpoint. The results indicate that prior exposure to 1 cGy of gamma rays reduces the number of chromatid breaks induced by subsequent higher dose (50 cGy), The expression of this adaptive response was similar among three cell lines despite of their different radiosensitivity. When 3-aminobenzamide, an inhibitor of poly (ADP-ribose) polymerase, was added after 50 cGy, adaptive responses were abolished in all the tested cell lines. Therefore it is suggested that the adaptive response can be observed in human lymphoblastoid cell lines, which was first documented through this study. The expression of adaptive response was similar among the cell lines regardless of their radiosensitivity. The elimination of the adaptive response by 3-aminobenzamide is consistent with the proposal that this adaptive response is the result of the induction of a certain chromosomal repair mechanism.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.2
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• pp.119-128
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• 2006

As dialogue systems are widely required, the research on natural language generation in dialogue has raised attention. Contrary to conventional dialogue systems that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains the answers to support more flexible and customized dialogues with humans. This paper proposes an evolutionary method for generating sentences using interactive genetic programming. Sentence plan trees, which stand for the sentence structures, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is obtained. The proposed method applies to a dialogue-based train booking agent and the usability test demonstrates the usefulness of the proposed method.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.131-143
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• 2007

The current trends in information technology and intelligent systems use data mining techniques to discover patterns and extract rules from distributed databases. In distributed environment, the extracted rules from data mining techniques can be used in dynamic replications, adaptive load balancing and other schemes. However, transmission of large data through the system can cause errors and unreliable results. This paper proposes the intelligent distributed platform based on dynamic group binding using mobile agents which addresses the use of intelligence in distributed environment. The proposed grouping service implements classification scheme of objects. Data compressor agent and data miner agent extracts rules and compresses data, respectively, from the service node databases. The proposed algorithm performs preprocessing where it merges the less frequent dataset using neuro-fuzzy classifier before sending the data. Object group classification, data mining the service node database, data compression method, and rule extraction were simulated. Result of experiments in efficient data compression and reliable rule extraction shows that the proposed algorithm has better performance compared to other methods.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1220-1227
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• 2003

To perform rational decision-making, autonomous agents need considerable computational resources. When other agents are present in the environment, these demands are even more severe. In these settings, it may be difficult for the agent to decide what to do in an acceptable time in multiagent situations that involve many agents. These problems motivate us to investigate ways in which the agents can be equipped with flexible decision-making procedures that enable them to function in a variety of situations in which decision-making time is important. The flexible decision-making methods explicitly consider a tradeoff between decision quality and computation time. Our framework limits resources used for agent deliberation and produces results that are not necessarily optimal, but provide autonomous agents with the best decision under time pressure. We validate our framework with experiments in a simulated anti-air defense domain. The experiments show that compiled rules reduce computation time while offering good performance.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.364-366
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• 2005

인터넷의 급격한 성장으로 인해 무선 네트워크에서의 인터넷 서비스 수요가 급증하고 있다. 이에 따라 시그널링 비용의 증가와 핸드오프 지연이 네트워크의 부하를 비롯, 통신 품질 저하 등의 문제점들을 야기했고, 이를 해결하기 위해 HMIPv6(Hierarchical Mobile IPv6)와 IP 페이징 기법들이 제안되고 있다. 본 논문에서는 HMIPv6 환경에서 이동노드의 특성을 고려하여 각각의 이동노드에게 적합한 MAP 도메인과 페이징 영역을 동시에 설정하는 기법(Adaptive Dynamic scheme)을 제안하여, 이때 PAMAP(Paging Agent MAP)이라는 새로운 개념을 제시한다. 이를 통해 동적 페이징에서의 불필요한 시그널링 비용을 줄임으로써 전체 네트워크의 부하를 줄여, 궁극적으로 통신 품질의 향상을 기대할 수 있다.