An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Fuzzy Cognitive Map based Prediction Tool for Schedule Overrun

The main aim of any software development organizations is to finish the project within acceptable or customary schedule and budget Software schedule overrun is one of a question that needs more concentration Schedule overrun may affect the whole project success like cost quality and increases risks Schedule overrun can be reason of project failure In today s competitive world controlling the schedule slippage of software project development is a challenging task Effective handling of schedule is an essential need for any software project organization The main tasks for software development estimation are determining the effort cost and schedule of developing the project under consideration Underestimation of project done knowingly just to win contract results into loses and also the poor quality project So precise schedule prediction leads to efficient control of time and budget during software development In this paper we developed a new technique for the prediction of schedule overrun This paper also presents the comparison with other algorithms of schedule estimation and Tool developed by us and at last proved that Fuzzy cognitive map based prediction tool gives more accurate results than other training algorithm

Interactive Problem Structuring with ICZM Stakeholders

Integrated Coastal Zone Management (ICZM) is struggling with a lack of science-management integration. Many computer systems, usually known as “decision support systems”, have been developed with the intention to make scientific knowledge about complex systems more accessible for coastal managers. These tools, allowing a multi-disciplinary approach with multi-criteria analyses, are designed for well-defined, structured problems. However, in practice stakeholder consensus on the problem structure is usually lacking. Aim of this paper is to explore the practical opportunities for the new so-called Quasta approach to structure complex problems in a group setting. This approach is based on a combination of Cognitive Mapping and Qualitative Probabilistic Networks. It comprehends a new type of computer system which is quite simple and flexible as well. The tool is tested in two workshops in which various coastal management issues were discussed. Evaluations of these workshops show that (1) this system helps stakeholders to make them aware of causal relationships, (2) it is useful for a qualitative exploration of scenarios, (3) it identifies the quantitative knowledge gaps of the problem being discussed and (4) the threshold for non technicians to use this tool is quite low.Integrated Coastal Zone Management, Problem Structuring, Stakeholder Participation, Cognitive Mapping, Interactive Policy Making

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

The decision-making process is highly demanding. There has been an increasing tendency to incorporate human thinking, individual experience about a problem, and pure mathematical approaches. Here, a novel integrated approach is investigated and proposed to develop an advanced hybrid decision-support system based on the decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCMs). Indeed, knowledge acquisition and elicitation may present distortions and difficulties finding a consensus and an interpretation. Thus, the proposed combined approach aims to examine in depth the potential to improve FCMs' outcomes by integrating FCM with the DEMATEL approach. The combined methodology achieves at avoiding some of the drawbacks, such as the lack of a standardized FCM theoretical model. Thus, it provides advanced comparative analysis and results in better interpretation of the decision-making process. It is highlighted that the traditional FCM approach does not allow distinguishing the whole number of defined scenarios, in contrast to the hybrid one presented here, which increases the ability of users to make correct decisions. Combining the two approaches provides new capabilities to FCMs in grouping experts' knowledge, while the DEMATEL approach contributes to refining the strength of concepts' connections

Why Modeling Complex Dynamic Systems using Fuzzy Cognitive Maps?

The difficult problem of modeling Complex Dynamic Systems (CDS) is carefully reviewed. Main characteristics of CDS are considered and analyzed. Today’s mathematical models and approaches cannot provide satisfactory answers to the challenging problems of the society. The key problem of complex dynamic systems and control theory consists in the development of methods of qualitative analysis of the dynamics and behavior of such systems and in the construction of efficient control algorithms for their efficient operation. The purpose of control to bring the system to a point of its phase space which corresponds to maximal or minimal value of the chosen efficiency criterion is reviewed and analyzed. The reasons for using Fuzzy Cognitive Maps (FCMs) in modeling Complex dynamic Systems are provided. The basics of FCMs are briefly presented. An illustrative example is considered and interesting results are presented and discusse

Why Modeling Complex Dynamic Systems using Fuzzy Cognitive Maps?

The difficult problem of modeling Complex Dynamic Systems (CDS) is carefully reviewed. Main characteristics of CDS are considered and analyzed. Today’s mathematical models and approaches cannot provide satisfactory answers to the challenging problems of the society. The key problem of complex dynamic systems and control theory consists in the development of methods of qualitative analysis of the dynamics and behavior of such systems and in the construction of efficient control algorithms for their efficient operation. The purpose of control to bring the system to a point of its phase space which corresponds to maximal or minimal value of the chosen efficiency criterion is reviewed and analyzed. The reasons for using Fuzzy Cognitive Maps (FCMs) in modeling Complex dynamic Systems are provided. The basics of FCMs are briefly presented. An illustrative example is considered and interesting results are presented and discussed