• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.1-9
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• 2005

In this paper, we suggest multi colony interaction ant reinforcement learning model. This method is a hybrid of multi colony interaction by elite strategy and reinforcement teaming applying Temporal Difference(TD) learning to Ant-Q loaming. Proposed model is consisted of some independent AS colonies, and interaction achieves search according to elite strategy(Intensification, Diversification strategy) between the colonies. Intensification strategy enables to select of good path to use heuristic information of other agent colony. This makes to select the high frequency of the visit of a edge by agents through positive interaction of between the colonies. Diversification strategy makes to escape selection of the high frequency of the visit of a edge by agents achieve negative interaction by search information of other agent colony. Through this strategies, we could know that proposed reinforcement loaming method converges faster to optimal solution than original ACS and Ant-Q.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.617-624
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• 2001

With the proliferation of E-Commerce, product informations and services are provided to customers diversely. Thus customers want a software agent that can retrieve and recommend goods satisfying various purchase conditions as well as price. In this paper, we present a MAS (multi-agent system) for book information retrieval and recommendation in E-Commerce. User's preference is reflected in the MAS using the profile which is taken by user. The proposed MAS is composed of individual agents that support information retrieval, information recommendation, user interface, and web robots and a coordination agent which performs information sharing and job management between individual agents. Our goal is to design and implement this multi-agent system on a Windows NT server. Owing to the workflow management of the coordination agent, we can remove redundant information retrievals of web robots. From the results, we could provide customers various purchase conditions for several online bookstores in real-time.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.731-740
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• 2004

In this paper we propose a fast negotiation agent system that guarantees the reciprocity of the attendants in a bilateral negotiation on the e-commerce. The proposednegotiation agent system exploits the incremental learning method based on an artificial neural network in generating a counter-offer and is trained by the previous offer that has been rejected by the other party. During a negotiation, the software agents on behalf of a buyer and a seller negotiate each other by considering the multi-attributes of a product. The experimental results show that the proposed negotiation system achieves better agreements than other negotiation agent systems that are operated under the realistic and practical environment. Furthermore, the proposed system carries out negotiations about twenty times faster than the previous negotiation systems on the average.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.37-45
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• 2022

In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.36-45
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• 2011

An agent in collaborative e-learning has independent function for learners in any circumstance, status and task by the reasonable and general means for social learning. In order to perform it well, communication among agents requires standardized and regular information technology method. This study suggests data model as a communication tool for various agents. Therefore this study shows various agents types for collaborative learning, designation of rule for data model that enable to communicate among agents and data element of agent communication data model. A multi-agent e-learning system using like this standardized data model should able to exchange the message that is needed for communication among agents who can take charge of their independent tasks. This study should contribute to perform collaborative e-learning successfully by the application of communication data model among agents for social learning.

• Korean Journal of Food Science and Technology
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• v.29 no.6
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• pp.1131-1137
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• 1997

The production rates of leavening gases and textures of cookies were investigated with various chemical leavening agents(baking powders). The chemical leavening agents could be divided into three group of a fast-acting group such as potassium bicarbonate, tartaric acid, aluminium ammonium sulfate, and fumaric acid, a slow-acting group such as ammonium bicarbonate, sodium bicarbonate, $glucono-{\delta}-lactone$, and ammonium chloride. and a double-acting group such as anhydro monocalcium phosphate, disodium dihydrogenpyrophosphate, and aluminium potassium sulfate according to the different production rate of gases. The leavening rate of ammonium bicarbonate, which was the highest of all leavening agents used in this experiment, was 131.25%. But its after-taste in a cookie was not good due to the residual ammonia. $Glucono-{\delta}-lactone$ only had no after-taste. The higher leavening rate, the more peaks in texture profile graph. Ammonium bicarbonate showed the most peaks in this experiment. It was found that the number of peak had correlation with brittleness of cookies $(r^2=0.8176)$ and brittleness of cookies was different as to various chemical leavening agents.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.817-827
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• 2007

In the age of ubiquitous environment, huge number of devices and computing services are provided to users. Personalized service, which is modeled according to the character of each and every individual is of particular need. In order to provide various dynamic services according to user's movement, service unit and operating mode should be able to operate automatically with minimum user intervention. In this paper, we discuss the steps of offering approximate service based on user's request in ubiquitous environment. First, we present our simulator designed for modeling the physical resource and computing object in smart space - the infrastructure in ubiquitous. Second, intelligent agents, which we developed based on a FIPA specification compliant multi-agent framework will be discussed. These intelligent agents are developed for achieving the service goal through cooperation between distributed agents. Third, we propose an automated service discovery and composition method in heterogeneous environment using semantic message communication between agents, according to the movement by the user interacting with the service available in the smart space. Fourth, we provide personalized service through agent monitoring anytime, anywhere from user's profile information stored on handhold device. Therefore, our research provides high quality service more than general automated service operation.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.61-69
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• 2022

A multi-agent system is a system that aims at achieving the best-coordinated decision based on each agent's local decision. In this paper, we consider a multi agent-multi task assignment problem. Each agent is assigned to only one task and there is a completion probability for performing. The objective is to determine an assignment that maximizes the sum of the completion probabilities for all tasks. The problem, expressed as a non-linear objective function and combinatorial optimization, is NP-hard. It is necessary to design an effective and efficient solution methodology. This paper presents an approximation algorithm using submodularity, which means a marginal gain diminishing, and demonstrates the scalability and robustness of the algorithm in theoretical and experimental ways.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.1-11
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• 2015

A concept of Deep Level Situation Understanding is proposed to realize human-like natural communication (called casual communication) among multi-agent (e.g., humans and robots/machines), where the deep level situation understanding consists of surface level understanding (such as gesture/posture understanding, facial expression understanding, speech/voice understanding), emotion understanding, intention understanding, and atmosphere understanding by applying customized knowledge of each agent and by taking considerations of thoughtfulness. The proposal aims to reduce burden of humans in humans-robots interaction, so as to realize harmonious communication by excluding unnecessary troubles or misunderstandings among agents, and finally helps to create a peaceful, happy, and prosperous humans-robots society. A simulated experiment is carried out to validate the deep level situation understanding system on a scenario where meeting-room reservation is done between a human employee and a secretary-robot. The proposed deep level situation understanding system aims to be applied in service robot systems for smoothing the communication and avoiding misunderstanding among agents.

• Environmental Mutagens and Carcinogens
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• v.18 no.1
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• pp.1-8
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• 1998

Chemoprevention refers to the use of non-toxic chemical agents to prevent the neoplastic development by inhibiting, delaying, or reversing a multi-stage carcinogenesis. The primary goal of chemoprevention research is to identify or produce effective agents and strategies for clinical trials for applications to normal or high risk human populations. A large number of compounds have been tested for their possible chemopreventive activities, and it is of interest to note that many of them are naturally occurring substances. Thus, a variety of plant and vegetable constituents, particularly those included in our daily diet, have been found to possess substantial protective properties against experimental carcinogenesis. These substances, collectively known as dietary phytochemicals, exert their chemopreventive effects by influencing specific step(s) of multi-stage carcinogenesis: some inhibit metabolic activation or enhance detoxification of carcinogens, others interfere with covalent interactions between ultimate eloctrophilic carcinogens and the target cell DNA and still others may exert anti-promoting or anti-progressing effects. Mechanism-based interventions by use of safe dietary phytochemicals may provide one of the most practical and promising cancer chemopreventive strategies.