Product return is a critical but controversial issue. To deal with such a vague return problem, businesses must improve their information transparency in order to administrate the product return behaviour of their end users. This study proposes an intelligent return administration expert system (iRAES) to provide product return forecasting and decision support for returned product administration. The iRAES consists of two intelligent agents that adopt a hybrid data mining algorithm. The return diagnosis agent generates different alarms for certain types of product return, based on forecasts of the return possibility. The return recommender agent is implemented on the basis of case-based reasoning, and provides the return centre clerk with a recommendation for returned product administration. We present a 3C-iShop scenario to demonstrate the feasibility and efficiency of the iRAES architecture. Our experiments identify a particularly interesting return, for which iRAES generates a recommendation for returned product administration. On average, iRAES decreases the effort required to generate a recommendation by 70% compared to previous return administration systems, and improves performance via return decision support by 37%. iRAES is designed to accelerate product return administration, and improve the performance of product return knowledge management.

Wireless technology has become widely popular and an important means of communication. A key issue in delivering wireless services is the problem of congestion which has an adverse impact on the Quality of Service (QoS), especially timeliness. Although a lot of work has been done in the context of RRM (Radio Resource Management), the deliverance of quality service to the end user still remains a challenge. Therefore there is need for a system that provides real-time services to the users through high assurance. We propose an intelligent agent-based approach to guarantee a predefined Service Level Agreement (SLA) with heterogeneous user requirements for appropriate bandwidth allocation in QoS sensitive cellular networks. The proposed system architecture exploits Case Based Reasoning (CBR) technique to handle RRM process of congestion management. The system accomplishes predefined SLA through the use of Retrieval and Adaptation Algorithm based on CBR case library. The proposed intelligent agent architecture gives autonomy to Radio Network Controller (RNC) or Base Station (BS) in accepting, rejecting or buffering a connection request to manage system bandwidth. Instead of simply blocking the connection request as congestion hits the system, different buffering durations are allocated to diverse classes of users based on their SLA. This increases the opportunity of connection establishment and reduces the call blocking rate extensively in changing environment. We carry out simulation of the proposed system that verifies efficient performance for congestion handling. The results also show built-in dynamism of our system to cater for variety of SLA requirements.

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide] efficient traffic regulation. Therefore, dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This study devised a multi-agent architecture, the Adaptive and Cooperative Traffic light Agent Model (ACTAM), for a decentralized traffic signal control system. The proposed architecture comprises a data storage and communication layer, a traffic regulation factor processing layer, and a decision-making layer. This study focused on utilizing the cooperation of multi-agents and the prediction mechanism of our architecture, the Forecast Module, to forecast future traffic volume in each individual intersection. The Forecast Module is designed to forecast traffic volume in an intersection via multi-agent cooperation by exchanging traffic volume information for adjacent intersections, since vehicles passing through nearby intersections were believed to significantly influence the traffic volume of specific intersections. The proposed architecture can achieve dynamic traffic signal control. Thus, total delay time of the traffic network under ACTAM can be reduced by 37% compared to the conventional fixed sequence traffic signal control strategy. Consequently, traffic congestion in urban areas can be alleviated by adopting ACTAM.

We describe a new inconsistent case which is susceptible to occur while producing consistent answer set using prioritized default logic. We define new semantics for prioritized default logic in order to solve this problem. There is a sign difference between General and Extended logic programs. Extended logic programs are formulated using classical negation, For this reason, an inconsistent answer set can sometimes be produced. For the most part, default reasoning semantics successfully resolved this problem, but a conflict could still arise in one particular case. The purpose of this paper is to present this eventuality, and revise the semantics of default logic in order to give an answer to this problem.

Modeling a complicated system as a multi-agent system is one of the most promising ways of designing a large, complex system. If we can assume that each agent in a multi-agent system has mental states (beliefs, knowledge, desires and so on), we can formalize each agent's behaviors in an abstract way without being bothered by system implementation details. We present semantic structures that are useful for representing belief states in multi-agent environments. One of the structures is a restriction of partial Kripke structures studied by Jaspars and Thijsse: we assume that each agent can access from a state of a structure to at most one state. We call the restricted structures only-child partial Kripke structures. We show some properties of only-child partial Kripke structures. Another structure is a restriction of the alternate nonstandard structures defined by Fagin et al. to deal with the logical-omniscience problem. We show several relationships between partial Kripke structures and the restriction of alternate nonstandard structures. Using the results, we show that the outputs of a belief estimation algorithm we previously developed can be characterized by using only-child partial Kripke structures. Finally, we show that only-child partial Kripke structures are more appropriate for the belief estimation problem than the restricted nonstandard structures.

Effective collaboration in ComputerSupported Collaborative Learning (CSCL) environments is nowadays an important research topic. It deals with two main problems: the configuration of an appropriate learning group and the intelligent task distribution in the practice of domain knowledge. In order to have effective collaboration in a CSCL environment, we have proposed a set of software agents that assist the learners to select their learning tasks, according to their capabilities and the possibilities of collaboration between them. In this paper the cooperation among software agents is presented as the key point for effective collaboration in CSCL environments. In this kind of environments the learner must have enough collaboration and learning possibilities, being motivated with the experience of social knowledge construction. We have been working on the problem of effective collaboration in CSCL environments, based on the cooperation between software agents developed for GRACILE, our Japanese Grammar CSCL environment. Before, we have proposed intelligent agents that assist the learners. Our next step has been the design of the cooperation between agents in order to create possibilities of effective collaboration in a virtual community of practice. In order to evaluate the performance of our agents we made several simulations. The results obtained from these simulations of diverse types of learning groups provided us with guidelines for the configuration of groups in CSCL environments, where effective collaboration is possible.

Intelligent Network (IN) is a distributed architecture allowing telecom companies to create and to customize services. Network and services have to be integrated in the management process provided by the IN. The integration of the Intelligent Network and the TMN (Telecommunications Management Networks) can be useful to achieve the management process. Our proposal is an evolution towards a more intelligent management structure provided through the Distributed Artificial Intelligence concepts.