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INTELLIGENT TECHNIQUES FOR E
INTELLIGENT TECHNIQUES FOR E

... 1998], some important characteristics/methods to represent a CBR-based negotiation process are: active or passive agent method, single or multiple dimension modification, over- or under- specification of customer demands. The methods are explained here. Active agents explicitly suggest refinements/m ...
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... Warshall’s Algorithm Constructs transitive closure T as the last matrix in the sequence of n-by-n matrices R(0), … , R(k), … , R(n) where R(k)[i,j] = 1 iff there is nontrivial path from i to j with only first k vertices allowed as intermediate Note that R(0) = A (adjacency matrix), R(n) = T (transi ...
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... can be quite successful. However, more direct methods have been considered in the literature recently that aim to target unplannability in particular. The first clear attempt to solve problems of unplannability was by Bäckström, Jonsson, and Ståhlberg (2013). In this work, Bäckström et al. cons ...
introduction to s-systems and the underlying power-law
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... This approach too has its attractions. It has been shown to provide a good approximation for many specific processes, and there are well-recognized procedures for estimating the parameter values in simple cases. One of the principal disadvantages lies in the fact that it is not a general formalism - ...
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... vector machines; array machines; multidimensional structured model of computation and computing systems; systolic arrays; hardware modification machines; Post productions; normal Markov algorithms; formal grammars of many forms – regular, context-free, context-sensitive, phrase-structure, etc.; and ...
The Multiple Knapsack Problem Approached by a Binary Differential
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... particle swarm optimization [3], the binary artificial fish swarm algorithm [4], and the binary fruit fly optimization algorithm [5]. In this work, it is investigated the performance of an adaptive Differential Evolution algorithm designed for binary problems. The Differential Evolution (DE) algorit ...
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konishi_condor_knopp.. - Computer Sciences Dept.

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Research Statement - Singapore Management University
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... represented by agents, and machine timeslots are treated as resources bidded by job agents through an auctioneer. Given the temporal dependency between tasks, resource bids are combinatorial. I developed different algorithms for achieving general equilibrium and winner determination. One such effort ...
Course title Instructor: , Associate Professor, NYUMC Center for Health Informatics & Bioinformatics
Course title Instructor: , Associate Professor, NYUMC Center for Health Informatics & Bioinformatics

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... Nowadays, temporal logics play an important role in theories of agency, an area where philosophy and computer science meet with other disciplines, such as game theory. Over the last decades, various paradigms have emerged for studying agents that process information and acquire knowledge while actin ...
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Chapter 8: Dynamic Programming
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... amount of money subject to the constraint that no two coins adjacent in the initial row can be picked up. E.g.: 5, 1, 2, 10, 6, 2. What is the best selection? ...
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Exam and Answers for 1999/00
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... b) Describe the idea behind simulated annealing? Simulated Annealing (SA) is motivated by an analogy to annealing in solids. The idea of SA comes from a paper published by Metropolis etc al in 1953 [Metropolis, 1953). The algorithm in this paper simulated the cooling of material in a heat bath. This ...
Fuzzy logic and neural networks
Fuzzy logic and neural networks

... pedagogical style. and starts from the very basic notion of neural computing and fuzzy logic learning to the advanced theory of cognitive neural computing. 2. The Neuron: Synaptic and Somatic Neural Operations Nature has developed a v u y complex neural smcture in most biological species [2-31. The ...
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Natural computing

Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.Computational paradigms studied by natural computing are abstracted from natural phenomena as diverse as self-replication, the functioning of the brain, Darwinian evolution, group behavior, the immune system, the defining properties of life forms, cell membranes, and morphogenesis. Besides traditional electronic hardware, these computational paradigms can be implemented on alternative physical media such as biomolecules (DNA, RNA), or trapped-ion quantum computing devices.Dually, one can view processes occurring in nature as information processing. Such processes include self-assembly, developmental processes, gene regulation networks, protein-protein interaction networks, biological transport (active transport, passive transport) networks, and gene assembly in unicellular organisms. Efforts tounderstand biological systems also include engineering of semi-synthetic organisms, and understanding the universe itself from the point of view of information processing. Indeed, the idea was even advanced that information is more fundamental than matter or energy. The Zuse-Fredkin thesis, dating back to the 1960s, states that the entire universe is a huge cellular automaton which continuously updates its rules.Recently it has been suggested that the whole universe is a quantum computer that computes its own behaviour.
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