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EN
The paper is concerned with allocation problems for a class of parallel operations described by a relational knowledge representation. Unknown parameters in the relations are assumed to be values of uncertain variables described by certainty distributions given by an expert. Theorems concerning properties of the optima! allocation are presented. The equivalence of the solutions obtained by a direct approach to the allocation problem and by a decomposition is discussed. An example illustrates the presented method.
EN
The paper is concerned with the problem of a knowledge-based resource distribution in a group of research units. The uncertain parameters in the relational knowledge representation are assumed to be values of uncertain variables characterized by an expert in the form of certainty distributions. Two versions of the problem have been formulated. In the second version, a "productivity" of the units in the former period and the estimation of the current possibilities presented by the expert are taken into account. The results and examples concerning the second version have been presented.
EN
The paper concerns task and resource allocation in a complex of operations which may be considered as a part of the knowledge-based project management system. The brief overview of concepts and results concerning the allocation problem under uncertainty described by uncertain variables is presented. An application of two-level decomposition of the complex and the allocation taking into account uncertain and random parameters in the description of the operations are discussed. Two simple examples illustrate the approach presented.
EN
The paper is concerned with recognition problems based on relational knowledge representations with two kinds of unknown parameters: uncertain parameters characterized by certainty distributions given by an expert and random parameters described by probability distributions. After a short presentation of uncertain variables and their application to the knowledge-based recognition under uncertainty, the different versions in two formulations of the recognition problem are discussed. In the first formulation two unknown parameters (uncertain and random) in the relation describing the set of object to be recognized are considered. The second formulation concerns the case with one unknown parameter in the knowledge representation, described by a certainty distribution with a random parameter or by a probability distribution with an uncertain parameter. Simple examples illustrate the presented approach. The application of so called C-uncertain variables and the description of the recognition system with three-level uncertainty are included.
EN
Two approaches to a decision problem for an uncertain plant are considered. In the descriptive approach an expert gives the knowledge of the plant which is used to determine the knowledge of the decision making. In the prescriptive approach the knowledge of the decision making is given directly by an expert. The basic idea of the comparison of these two approaches is presented in the paper for two descriptions of the uncertainty, based on uncertain variables and on fuzzy formalism. A simple example and results of simulations are described.
EN
For analysis and decision problems in a class of uncertain systems a concept of uncertain variables has been elaborated. In this paper it has been shown how the uncertain variables may be applied to an allocation problem for a class of parallel operations described by a relational knowledge representation with unknown parameters. The unknown parameters are characterized by certainty distributions given by an expert.
EN
The nonlinear and time-varying uncertain system with a constant unknown vector of parameters is considered. The unknown parameter is assumed to be a value of an uncertain variable described by a certainty distribution given by an expert. The estimation of the certainty index that the system is globally asymptotically stable based on the necessary and sufficient stability conditions is proposed and considered. A simple example illustrates the approach presented.
EN
The paper deals with a specific class of knowledge-based systems with a dynamical plant described by a knowledge representation with unknown parameters. The learning consists of step by step knowledge evaluation and updating, and using the results for the determination of the current control decisions. The paper presents an extension of methods and algorithms described by the author (1999) for the knowledge-based learning systems with a static plant. Two forms of the plant descriptions are taken into account: the relational knowledge representation, and the logical knowledge representation. A simple illustrative example is given.
EN
The paper is concerned with a production system consisting of parallel production sections. The allocation problem consists in the distribution of a raw material among the production sections taking into account not only the production cost (the production time) but also the costs of the transport of the raw material from one store to the sections and of a product from the sections to one store. The control algorithm for two sections is presented in the first part. In the next parts the conditions of equivalence are formulated. If those conditions are satisfied, the problem of minimizing the global cost is reduced to the known allocation problem for the complex of parallel operations.
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