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Interactive Dynamic Influence Diagrams (I-DIDs) constitute a graphic model for multi-agent decision making under uncertainty, but solving them is provably intractable. Algorithms for solving I-DIDs face the challenge of an exponentially growing space of candidate models ascribed to other agents, over time. Pruning behaviorally equivalent models is one way toward minimizing the model set, but composing...
This paper studies robust knapsack problems, for which solutions are, up to a certain point, immune to data uncertainty. We complement the works found in the literature where uncertainty affects only the profits or only the weights of the items by studying the complexity and approximation of the general setting with uncertainty regarding both the profits and the weights, for multiple objective functions...
We give efficient algorithms for volume sampling, i.e., for picking k-subsets of the rows of any given matrix with probabilities proportional to the squared volumes of the simplices defined by them and the origin (or the squared volumes of the parallelepipeds defined by these subsets of rows). This solves an open problem from the monograph on spectral algorithms by Kannan and Vempala (see Section...
Rough set theory is emerging as a powerful tool for reasoning about data. Attribute reduction is one of important topics in the research on the rough set theory. The classical rough set theory based on equivalence relation has made a great progress, while the equivalence relation is too harsh to meet and is extended to tolerance relation in real world. It is important to investigate rough computational...
This paper considers a special case of the coupled-tasks scheduling problem on one processor. The general problems were analyzed in depth by Orman ad Potts. In this paper, we consider that all processing times are equal to 1, the gap has exact length L, we have precedence constraits, compatibility constraits are introduced and the criterion is to minimize the scheduling length. We use this problem...
Counting solutions of various combinatorial problems is a well established and intensively studied area of theoretical computer science. Its applications range from networking to statistical physics. The central question in this area is the complexity of and algorithms for problems of this type. It turns out that in many important cases these questions can be answered with aid of certain clones of...
This paper presents theoretical results for mapping and scheduling linear workflows onto heterogeneous platforms. We use a realistic architectural model, representative of current multi-threaded systems. Our model has bounded communication capabilities and full computation/communication overlap. In these workflow applications, the goal is often to maximize throughput or to minimize latency. We present...
Concept lattice is widely used in data analysis. This paper aims to reduce redundant nodes from concept lattice by replacing lattice with join-semilattice. We develop an algorithm based on gradual insertion of concepts into partially hasse diagram. An example is proposed to illustrate the constructing procedure of concept semilattice. Experimental results show effectiveness and efficiency of the proposed...
Linear ordering problem (LOP) is a well know optimization problem attractive for its complexity (it is a NP hard problem), rich collection of testing data and variety of real world applications. In this paper, we investigate the usage and performance of two variants of genetic algorithms - mutation only genetic algorithms and higher level chromosome genetic algorithms - on the linear ordering problem...
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