• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.65-78
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• 2009

The rapid changes of financial environment have increased the need and demand for personal financial advisory service from financial experts. In particular, as the individual customers want to get more customized financial services, the financial institutions created the private banking (PB) sector and have constantly expanded their PB services. However, it remains still problematic that the private banking system requires high costs so that the number of eligible customers who can have proper PB services is quite limited. To solve this problem, we propose an intelligent agent that can provides specialized and customized personal financial advisory services to the customers with low costs. The proposed agent systemizes and structures the information and knowledge of financial experts in private banking services so that individual customers can easily access to high-quality PB services when they need. On the first attempt we develop a framework of U-smart PB, an intelligent agent for personal financial management based on different scenarios related to personal financial decisions, and derive its core services. This system not only provides information simply, but also proposes to support personal investment decisions technically as an intelligent agent that embodies real-time customized financial management in a ubiquitous environment, regardless of time and place.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.155-175
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• 2003

In context of e-commerce, negotiation is a procedure to help negotiate between buyer and seller by adjusting their negotiation issues such as price and in terms of payment. We used intelligent agent and mobile device to promote new framework of e-commerce. Moreover, this framework can help buyers and sellers to carry their commercial transactions effectively. In regard to that issue, we need to carry out the research of negotiation agent that can be used in e-commerce fields. In this paper, we modeled the negotiation using CSP for the performance of agent in m-commerce environment. Furthermore we implemented interface for mobile device to extract buyer's requirement and preference easily Besides that we used utility function to make a decision for various evaluation functions and suggestions that are used for evaluation of negotiation issues. A difficulty of generating offer is dependent on the number of negotiation issues and the range of the values. Therefore, if any offer has a number of negotiation issues and the range of values are wide, the search space will be exponentially expanded. There have been many studies fur solving this problem, we applied those techniques to improve the agent's ability of negotiation. For example, a contract can be accomplished by exchanging seller and buyer's offer that is generated by agent to adjust the requisite profit for each party. Finally, we show the improvement of satisfaction as the negotiation is processed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.750-762
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• 2019

The importance of the call center as a contact point for the enterprise is growing. However, call centers have difficulty with their operating agents due to the agents' lack of knowledge and owing to frequent agent turnover due to downturns in the business, which causes deterioration in the quality of customer service. Therefore, through an N-bank call center case study, we developed a system to reduce the burden of keeping up business knowledge and to improve customer service quality. It is a "real-time agent advisor" system that provides agents with answers to customer questions in real time by combining AI technology for speech recognition, natural language processing, and questions & answers for existing call center information systems, such as a private branch exchange (PBX) and computer telephony integration (CTI). As a result of the case study, we confirmed that the speech recognition system for real-time call analysis and the corpus construction method improves the natural speech processing performance of the query response system. Especially with name entity recognition (NER), the accuracy of the corpus learning improved by 31%. Also, after applying the agent advisor system, the positive feedback rate of agents about the answers from the agent advisor was 93.1%, which proved the system is helpful to the agents.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.545-549
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• 2007

The Bio-Inspired System focuses on the creation of an effective system model for massive network applications and is being widely developed. However, the system has a problem-difficulty implementing three features in the system, which includes scalability, adaptability and survivability. To solve this problem, we designed an Ecogent as a multiple intelligence agent, and a Bio-platform to address the three features of scalability, adaptability and survivability. The Bio-Inspired System Platform consists of an ERS (Ecogent Runtime Services) Platform and a Bio-Platform. The ERS platform serves the basic functions of mobile agents, such as Registration, Life Cycle, Migration, Communication, Location and Fault Tolerance. The Bio-Platform includes the functions of Evolution Control and Stigmergy Control to address evolution and adaptation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.129-139
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• 2005

The rapid growth of IT technologies enables the quality of human's daily life to be improved dramatically, Contrasting to the services in previous computing environment directed by user's request, the services in ubiquitous computing era of new IT technology are provided through recognizing users intention and utilizing context-aware information suited to the user. According to the contextual information, agents need to set a dynamic goal to achieve and work collaboratively with other agents. Agents that take control over their behaviors with capability of communicating with other agents become a thrust in this up-coming computing environment. This work focuses on building ontologies, shared knowledge bases among agents, to improve semantical interoperability among agents. More attention is given to the construction and effective management of ontology repository along with its requirement and organization. Ontology agent suggested takes an initiative role to manage the repository in a distributed manner and to facilitate the use of ontology in a multi-agent environment.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.169-185
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• 2011

Data redundancy is problematic in that it not only induces heavy storage management cost but also could bring critical degradation of information systems. Unfortunately, to our knowledge, only few enterprises willingly afford time and efforts for the faithful conceptual design to prevent the degree of inappropriate data as much as they could, while most of enterprises pay rare attention to the notion of that sort of data quality. Wondering if there would be any other way to design the enterprise.wide data design without prior knowledge about business works is our major motivation for this study. In this paper, we present our data modeling methodology in which associations among objects in each sentences of a business job descriptions are treated as the focal point in database design. A proposed agent for automated design tool simply takes a business job description written in natural language as an input, and then designs an entity relationship diagram with some smart rules. We introduce the scope of the proposed agent and its detailed logics with several examples. And then, we verify the appropriateness of the resulted associations among objects. Lastly, we perform case studies to evaluate the devised agent's applicability to a business field.

• 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 Intelligence and Information Systems
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• v.12 no.2
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• pp.17-32
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• 2006

Recently, community computing has been proposed for group formation and group decision-making. However, legacy community computing systems do not support group need identification for ad hoc group formation, which would be one of key features of ubiquitous decision support systems and services. Hence, this paper aims to provide a multi-agent based methodology to enable nomadic community computing which supports ad hoc need identification and group formation. Focusing on supporting group decision-making of relatively small sized multiple individual in a community, the methodology copes with the following three characteristics: (1) ad hoc group formation, (2) context-aware group need identification and (3) using mobile devices working in- and out-doors. NAMA-US, an RFID-based prototype system has been developed to show the feasibility of the idea proposed in this paper.