Sample Average Approximation of Expected Value Constrained
... also verified the effectiveness of the SAA approach for stochastic programs of the form (5). See [11] and references therein for further details. In this paper we investigate an SAA method for expected value constrained problems (1). We require the expected value constraint in (1) to be soft, i.e., ...
... also verified the effectiveness of the SAA approach for stochastic programs of the form (5). See [11] and references therein for further details. In this paper we investigate an SAA method for expected value constrained problems (1). We require the expected value constraint in (1) to be soft, i.e., ...
Optimal Allocation Strategies for the Dark Pool Problem
... given value. They show that the allocations of their algorithm are -suboptimal with probability at most 1 − after seeing sufficiently many samples. Theorem 1 in Ganchev et al. (2009) shows that, if the sti is chosen iid, then the optimal strategy always allocates the ith unit to a fixed venue. Th ...
... given value. They show that the allocations of their algorithm are -suboptimal with probability at most 1 − after seeing sufficiently many samples. Theorem 1 in Ganchev et al. (2009) shows that, if the sti is chosen iid, then the optimal strategy always allocates the ith unit to a fixed venue. Th ...
Algorithms and Arguments Artificial Intelligence
... contrivances at present known or likely to be discovered really deserve the name of logical machines. It is but a very small part of the entire process, which goes to form a piece of reasoning, which they are capable of performing. For, if we begin from the beginning, that process would involve four ...
... contrivances at present known or likely to be discovered really deserve the name of logical machines. It is but a very small part of the entire process, which goes to form a piece of reasoning, which they are capable of performing. For, if we begin from the beginning, that process would involve four ...
MULTICAST RECIPIENT MAXIMIZATION PROBLEM IN 802 16
... The trends of both figures are very similar, The trends of both figures are very similar, meaning that DSS provides same satisfying performance under different radio conditions. ...
... The trends of both figures are very similar, The trends of both figures are very similar, meaning that DSS provides same satisfying performance under different radio conditions. ...
Rollout Sampling Policy Iteration for Decentralized POMDPs
... Another key question is how to choose the heuristic policies. In fact, the usefulness of the heuristics and, more importantly, the computed belief states, are highly dependent on the specific problem. Instead of just using one heuristic, a whole portfolio of heuristics can be used to compute a set o ...
... Another key question is how to choose the heuristic policies. In fact, the usefulness of the heuristics and, more importantly, the computed belief states, are highly dependent on the specific problem. Instead of just using one heuristic, a whole portfolio of heuristics can be used to compute a set o ...
Planning with Specialized SAT Solvers
... increased the performance still further, now surpassing the performance of best existing planners based on any search method. This second variant differs in two respects. First, its depth-first search is not terminated after one action is found, but proceeds further to identify several actions (10 in ...
... increased the performance still further, now surpassing the performance of best existing planners based on any search method. This second variant differs in two respects. First, its depth-first search is not terminated after one action is found, but proceeds further to identify several actions (10 in ...
Artificial Neural Networks (ANN), Multi Layered Feed Forward (MLFF
... GMDH type neural networks can overcome these problems. It can pick out knowledge about object directly from data sampling. The GMDH is the inductive sorting-out method, which has advantages in the cases of rather complex objects, having no definite theory, particularly for the objects with fuzzy cha ...
... GMDH type neural networks can overcome these problems. It can pick out knowledge about object directly from data sampling. The GMDH is the inductive sorting-out method, which has advantages in the cases of rather complex objects, having no definite theory, particularly for the objects with fuzzy cha ...
Artificial Intelligence Experimental results on the crossover point in
... Further, many commercially important problems in scheduling, configuration, and planning also appear to be instances of NP-complete problems. The best-known algorithms for solving such problems are known to require exponential run time (in the size of the problem) in the worst case. However, a worst ...
... Further, many commercially important problems in scheduling, configuration, and planning also appear to be instances of NP-complete problems. The best-known algorithms for solving such problems are known to require exponential run time (in the size of the problem) in the worst case. However, a worst ...
1+c1*φ
... chromatographic separations and for metabolite identification by the majority of chromatographers without some experience or knowledge of programming Microsoft Excel is a user-friendly environment due to its unique features in organizing, storing and manipulating data using basic and complex mathe ...
... chromatographic separations and for metabolite identification by the majority of chromatographers without some experience or knowledge of programming Microsoft Excel is a user-friendly environment due to its unique features in organizing, storing and manipulating data using basic and complex mathe ...
Current and Future Trends in Feature Selection and Extraction for
... In many classification task domains the given features are not sufficient to achieve acceptable classification performance, but a transformation of the features may yield new features that are more highly correlated with the class value. In this section we describe several methods for extracting fea ...
... In many classification task domains the given features are not sufficient to achieve acceptable classification performance, but a transformation of the features may yield new features that are more highly correlated with the class value. In this section we describe several methods for extracting fea ...
Advanced Research into AI Ising Computer (PDF format, 212KB)
... is not the overall minimum for the system. To escape such local minima, the spin states are randomly perturbed. This causes the system to randomly switch to an unrelated state, as indicated by the dotted line in Fig. 3. Collectively, these two processes are called CMOS annealing. By using them, it i ...
... is not the overall minimum for the system. To escape such local minima, the spin states are randomly perturbed. This causes the system to randomly switch to an unrelated state, as indicated by the dotted line in Fig. 3. Collectively, these two processes are called CMOS annealing. By using them, it i ...
CAPTCHA: Using Hard AI Problems For Security
... not have algorithms for that problem that are are much better than the stateof-the-art algorithms, and then prove a reduction between passing a test and exceeding the performance of state-of-the-art algorithms. In the case of ordinary cryptography, it is assumed (for example) that the adversary can ...
... not have algorithms for that problem that are are much better than the stateof-the-art algorithms, and then prove a reduction between passing a test and exceeding the performance of state-of-the-art algorithms. In the case of ordinary cryptography, it is assumed (for example) that the adversary can ...
Solving Complex Logistics Problems with Multi
... Supply chain researchers have applied various complementary approaches so as to resolve problems in collaboration, including optimization-based, multi-agentbased, and simulation-based. Each approach has unique strengths, but only identifies optimal solutions for given situation subject to specific a ...
... Supply chain researchers have applied various complementary approaches so as to resolve problems in collaboration, including optimization-based, multi-agentbased, and simulation-based. Each approach has unique strengths, but only identifies optimal solutions for given situation subject to specific a ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.