Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order to
This article deals with how metaknowledge can improve rule-based system and presents a new Reflexive System Inference Engine (RSIE) which not only enables the activation of rules, making it belong to systems managing metaknowledge. The experimentation section shows a rulebased system named IDRES with a structure which has been modified to use metaknowledge.
The goal of Active Template research is to create a single, unified environment that a data analyst can use to carry out a knowledge discovery project, and to deliver the resulting solution in the form of an Active Template. An Active Template is a hyper-linked information structure that tightly integrates actions (executable programs and commands), results (models, datasets, predictions, reports), and documentation (explanations of decisions, actions, and results). The use of Active Templates provides a number of benefits, including user guidance, improved documentation of actions and results, and increased reuse of previous work.
Every time someone gets a computer or a robot to play or solve a new task or problem, a lot of people come out to remind us that the activity of the machine is not a genuine intelligence (that is, supposedly like ours): it would be a simple computation carried out thanks to the human capacity to program something that does not cease to be a piece of silicon, sheet metal, and wires. This is called "Artificial Intelligence effect" and is widely extended. No matter the feat achieved by the machine: if it defeats the chess world champion, it is taken as a mere computation (remarkable, yes, but nothing to do with real intelligence). We do not accept that there is a genuine intelligence as far as we can understand how the machine works to do something or give an answer to a problem. Not to talk about attributing consciousness to a supercomputer or assuming that it could suffer from mental illnesses (this would be the case if having a mind).
This paper shows how a new approach in the use of AI techniques has been successfully used for the design of an effective ITS in the domain of diagnosis training. The originality of this approach was to take into account three complex problems simultaneously: teaching diagnosis methods to students, giving the means to the teachers of maintaining the system by themselves and providing a tool easy to insert in the context of university laboratories. The architecture of the system is based on a distinct use of two kinds of knowledge representation. All the knowledge liable to modifications is gathered within libraries under descriptive forms easily maintained by the educational staff. General diagnosis knowledge independent of hardware, circuits and even application fields, is described with basic production rules and control metarules. The development of the system was based on the precise analysis of the expert's behaviour and of the user's needs, with the aim of making extensive use of the descriptive forms in order to minimize the static knowledge embedded in the rules. The system can work on a microcomputer and is used in an engineering school.