Knowledge evaluation for knowledge management implementation : : the case study of the radio-pharmaceutical center of IPEN

In recent years organizations are using multiple methods and approaches to design their strategic and action plans. In this context, Resource-based View (RBV) and Knowledge-based View (KBV) frameworks are receiving increased attention as instrumental to strategy formulation. The synergy of these approaches with Knowledge Management initiatives is intuitive and their use are in a common framework is discussed here to show the importance of methods and instruments to mapping and assessing the knowledge assets of the organization. The application of such methods to the Radio-pharmaceutical Center of IPEN is discussed in this paper.Knowledge management, Nuclear knowledge management

Many-Valued Institutions for Constraint Specification

We advance a general technique for enriching logical systems with soft constraints, making them suitable for specifying complex software systems where parts are put together not just based on how they meet certain functional requirements but also on how they optimise certain constraints. This added expressive power is required, for example, for capturing quality attributes that need to be optimised or, more generally, for formalising what are usually called service-level agreements. More specifically, we show how institutions endowed with a graded semantic consequence can accommodate soft-constraint satisfaction problems. We illustrate our approach by showing how, in the context of service discovery, one can quantify the compatibility of two specifications and thus formalise the selection of the most promising provider of a required resource.Peer Reviewe

Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions

Evaluating Critical Success Factors of Distributed Learning

Distributed learning presents universities and colleges with the ability to expand their reach into new markets and stay competitive and relevant in this dynamic information-based global economy. Through the effective use of distributed learning tools, location and cost are no longer barriers to earning a degree and enable universities and colleges to reach working adults and international students as well as further penetrate the traditional student market. This paper highlights the evolving transformation of Distance learning models to the evolving technology based distributed learning modes. While each institution has its own mission and goal for distance learning and distributed learning, there are certain things that need to be considered while developing or implementing a curriculum that involves education at a distance. This paper explores distance learning from a macro perspective and suggests some critical success factors that will aid faculty and institutions in distance learning and distributed learning development

Universal neural field computation

Turing machines and G\"odel numbers are important pillars of the theory of computation. Thus, any computational architecture needs to show how it could relate to Turing machines and how stable implementations of Turing computation are possible. In this chapter, we implement universal Turing computation in a neural field environment. To this end, we employ the canonical symbologram representation of a Turing machine obtained from a G\"odel encoding of its symbolic repertoire and generalized shifts. The resulting nonlinear dynamical automaton (NDA) is a piecewise affine-linear map acting on the unit square that is partitioned into rectangular domains. Instead of looking at point dynamics in phase space, we then consider functional dynamics of probability distributions functions (p.d.f.s) over phase space. This is generally described by a Frobenius-Perron integral transformation that can be regarded as a neural field equation over the unit square as feature space of a dynamic field theory (DFT). Solving the Frobenius-Perron equation yields that uniform p.d.f.s with rectangular support are mapped onto uniform p.d.f.s with rectangular support, again. We call the resulting representation \emph{dynamic field automaton}.Comment: 21 pages; 6 figures. arXiv admin note: text overlap with arXiv:1204.546