Theory of fuzzy integrals and its applications

What is modal logic

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications In the present paper, these two aspects are thoroughly reviewed and an extensive bibliography is provided Moreover, one section is devoted to newcomers It consists of steps for developing a heuristic for any particular problem Those steps are common to the implementation of other metaheuristics

Variable neighbourhood search: methods and applications

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this field.

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A survey on metaheuristics for stochastic combinatorial optimization

Some first-order probability logics

We introduce new types of probability operators of the form QF , whereF is a recursive rational subset of [0; 1]. A formulaQF is satisfied in a probability model if the measure of the set of worlds that satisfy is in F . The new operators are suitable for describing events in discrete sample spaces. We provide sound and complete axiomatic systems for a number of probability logics augmented with the QF -operators. We show that the new operators are not definable in languages of probability logics that have been used so far. We study decidability of the presented logics. We describe a relation of ‘being more expressive’ between the new probability logics.

Some probability logics with new types of probability operators

In this article, we introduce the probabilistic justification logic PJ, a logic in which we can reason about the probability of justification statements. We present its syntax and semantics, and establish a strong completeness theorem. Moreover, we investigate the relationship between PJ and the logic of uncertain justifications.

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First steps towards probabilistic justification logic

We introduce a propositional and a first-order logic for reasoning about discrete linear time and finitely additive probability. The languages of these logics allow formulae that say 'sometime in the future, α holds with probability at least s'. We restrict our study to so-called measurable models. We provide sound and complete infinitary axiomatizations for the logics. Furthermore, in the propositional case decidability is proved by establishing a periodicity argument for ω-sequences extending the decidability proof of standard propositional temporal logic LTL. Complexity issues are examined and a worst-case complexity upper bound is given. Extensions of the presented results and open problems are described in the final part of the paper.

Zoran Ognjanović

Papers

Some first-order probability logics

Some probability logics with new types of probability operators

First steps towards probabilistic justification logic

Discrete Linear-time Probabilistic Logics: Completeness, Decidability and Complexity

A logic with approximate conditional probabilities that can model default reasoning