Pozyskiwanie wiedzy dla zarządzania przepływem informacji

This paper addresses the problem of formal (quantitative) and soft (qualitative) modelling of a flow of information in autonomous systems that in a real life context are formed by sub-components consisting of people, machines, robots, etc. Models in management science are constructed and applied in order to describe, understand, and finally support processes and activities that are primarily intellectual. The problems addressed by these models may arise so frequently that the benefits of standardisation are sought, or else they may be one-of-a-kind situations of such importance that steps are taken to improve the quality of a decision that has been taken. In other words, models are developed mainly to create knowledge. This is also the main purpose of the modelling platform proposed in this paper.Autor zajmuje się problemem formalnego (ilościowego) i miękkiego (jakościowego) modelowania przepływu informacji w systemach autonomicznych, które w praktyce kształtowane są przez podsystemy składające się z ludzi, maszyn, robotów itd. W nauce o zarządzaniu modele konstruuje się i stosuje w celu opisania, zrozumienia, a wreszcie wsparcia procesów i działań, które mają charakter przede wszystkim intelektualny. Problemy, którymi zajmują się te modele mogą się pojawiać tak często, że poszukuje się korzyści wynikających ze standaryzacji lub mogą być sytuacjami jednostkowymi o takiej wadze, że podejmuje się kroki w celu poprawy jakości rezultatu podjętej decyzji. Inaczej mówiąc, modele opracowuje się głównie w celu stworzenia wiedzy. Jest to również główny cel platformy modelowania zaproponowanej w artykule

The acquisition of knowledge for managing the flow of information

Autor zajmuje się problemem formalnego (ilościowego) i miękkiego (jakościowego) modelowania przepływu informacji w systemach autonomicznych, które w praktyce kształtowane są przez podsystemy składające się z ludzi, maszyn, robotów itd. W nauce o zarządzaniu modele konstruuje się i stosuje w celu opisania, zrozumienia, a wreszcie wsparcia procesów i działań, które mają charakter przede wszystkim intelektualny. Problemy, którymi zajmują się te modele mogą się pojawiać tak często, że poszukuje się korzyści wynikających ze standaryzacji lub mogą być sytuacjami jednostkowymi o takiej wadze, że podejmuje się kroki w celu poprawy jakości rezultatu podjętej decyzji. Inaczej mówiąc, modele opracowuje się głównie w celu stworzenia wiedzy. Jest to również główny cel platformy modelowania zaproponowanej w artykule.This paper addresses the problem of formal (quantitative) and soft (qualitative) modelling of a flow of information in autonomous systems that in a real life context are formed by sub-components consisting of people, machines, robots, etc. Models in management science are constructed and applied in order to describe, understand, and finally support processes and activities that are primarily intellectual. The problems addressed by these models may arise so frequently that the benefits of standardisation are sought, or else they may be one-of-a-kind situations of such importance that steps are taken to improve the quality of a decision that has been taken. In other words, models are developed mainly to create knowledge. This is also the main purpose of the modelling platform proposed in this paper

Integration platform for multi-agent systems in information-rich environments

Engineering, operations research, and management science use scientific and engineering processes to design, plan, and schedule increasingly more complex systems functioning in increasingly more complex environments to enhance their performance. One can argue that the systems have grown in complexity over the years mainly due to increased striving for resource optimization combined with a greater degree of uncertainty in the system's environment. Information is seen as one of the main resources that systems analysts try to use in an optimal way. We show how this resource can be used in integration issues. We introduce the problem of information-based integration, propose a solution, and illustrate the proposed solution with an example

Hard and soft modelling based knowledge capture for information flow management

This chapter addresses the problem of formal (quantitative) and soft (qualitative) modelling of an information flow in autonomous systems that in real life context are formed by agents consisting of people, machines, robots, etc. Models in management science are designed applied to describe, understand, and finally support processes and activities that are primarily intellectual. The problems attacked by these models may arise so frequently that the benefits of routinization are sought or they may be one-of-a-kind situations of such importance that steps are taken to improve the quality for the decision outcome. In other words, models are developed mainly to create knowledge. This is also the main purpose of the modelling platform proposed in this Chapter

Decisional DNA, reflexive ontologies, and security: developing decisional trust

Semantic technologies constitute one of the most interesting technologies derived from the World Wide Web revolution. It is a field constantly reviewed in diffferent areas of knowledge and its greatest improvements for information and khowledge technologies are still there to be discovered. Within the myriads of semantic based techniques available, a great attention has been given to ontologies and how their implementation and use enhance real world applications that are not directly related to the Web itself. Ontologies offer great flexibility and capability to model specific domains, and hence, conceptualize the portion of reality to which such domain refers. Nevertheless, it is not enough to have a good modelled ontology fed with real world instances (individuals) from trustable sources of information; nowadays, it is of the utmost importance to enhance such technologies with decisional capabilities that can offer trustable knowledge in a fast way. On this regard, the introduction of concepts such as the Set of Experience Knowledge Structure (SOEKS or shortly SOE), Decisional DNA and Reflexive Ontologies (RO) lead to alternative technologies that can offer trustable knowledge

Using set of experience in the process of transforming information into knowledge

Some of the most complicated issues about knowledge are its acquisition and its conversion into explicit knowledge. Nevertheless, among all knowledge forms, storing formal decision events in a knowledge-explicit way becomes an important advance. The set of experience knowledge structure can help in achieving this purpose. Explicit knowledge offormal decision events emerges to help managers in decision-making because, usually, they use previous similar or equal decisions to help themselves in new decision-making processes. The Knowledge Supply Chain System (KSCS) is a platform proposed to administer formal decision events in a knowledge-explicit way. It is supported by Sets of Experience Knowledge Structure in the effective process of transforming information into knowledge. The purpose of this paper is to show how the set of experience knowledge structure is implemented into the KSCS. Fully developed, KSCS certainly would improve the quality ofdecision-making and could advance the notion of administering knowledge in the current decision-making environment

A decisional trust implementation on a maintenance system by the means of decisional DNA and reflexive ontologies

This article introduces the elements that we replaced together in order to achieve a decisional technology that can offer trust. Thus, we refer to it as Decisional Trust. Enhancing a decisional knowledge system with trust means that the user relies on what the system knows and that the system possesses a qualified reliance on received knowledge. Our proposal works in two fronts:(i) the construction of Reflexive Ontologies; and (ii)the construction of Decisional DNA. Then, we add trust models in order to gain certainty based upon the past decisional experience, and just until then, we can refer to the system as a Decisional Trust System. Additionally, an augmented reality (AR)maintenance system is improved by adding the elements of decisional trust. This framework improves the quality of decision-making, and advances the notion of administering trustable knowledge in the current decision making environment

Experience-based knowledge representation: SOEKS

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process. For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment