Information Gathering and Reward Exploitation of Subgoals for
... can tackle problems simultaneously requiring substantial information gathering and long planning horizons. Here we propose a point-based POMDP solution method, called Information Gathering and Reward Exploitation of Subgoals (IGRES), that effectively tackles both challenges by incorporating elements ...
... can tackle problems simultaneously requiring substantial information gathering and long planning horizons. Here we propose a point-based POMDP solution method, called Information Gathering and Reward Exploitation of Subgoals (IGRES), that effectively tackles both challenges by incorporating elements ...
A Comparative Illustration of AI Planning-based
... constraint rules must be checked, rule-based expert systems can work well. However, considering the fast growth of web services, building a full knowledge base by converting all web services into axioms, will be expensive. SWORD [Ponnekanti and Fox 2002] is an example of this approach. However, for ...
... constraint rules must be checked, rule-based expert systems can work well. However, considering the fast growth of web services, building a full knowledge base by converting all web services into axioms, will be expensive. SWORD [Ponnekanti and Fox 2002] is an example of this approach. However, for ...
description
... instances per domain. The instances are ordered by difficulty in terms of number of objects and/or the degree of randomness. For example, in the SysAdmin domain problems are ordered by the number of computers and the failure probability of each computer. A total time of 24 hours is divided uniformly ...
... instances per domain. The instances are ordered by difficulty in terms of number of objects and/or the degree of randomness. For example, in the SysAdmin domain problems are ordered by the number of computers and the failure probability of each computer. A total time of 24 hours is divided uniformly ...
A Genetic-Firefly Hybrid Algorithm to Find the Best Data Location in
... all values has a value comparable to the core cube, it is not necessary to visualize the core cube because responding to a query using the cube incurs the same cost as the core cube. Many greedy algorithms have been proposed to position data in data cube [16, 25]. Even if all data cubes must be calc ...
... all values has a value comparable to the core cube, it is not necessary to visualize the core cube because responding to a query using the cube incurs the same cost as the core cube. Many greedy algorithms have been proposed to position data in data cube [16, 25]. Even if all data cubes must be calc ...
Dynamic Programming
... At each location, Yuckdonald’s may open at most one restaurant. The expected profit from opening a restaurant at location i is pi > 0 Any two restaurants should be at least k miles apart, where k is a ...
... At each location, Yuckdonald’s may open at most one restaurant. The expected profit from opening a restaurant at location i is pi > 0 Any two restaurants should be at least k miles apart, where k is a ...
Audio Compression
... A query by humming system Two-dimensional: pitch and rhythm Comparison between string-alignment (edit cost) dynamic programming and HMM algorithms (each theme represented as a model) Also compared to human performance Results ...
... A query by humming system Two-dimensional: pitch and rhythm Comparison between string-alignment (edit cost) dynamic programming and HMM algorithms (each theme represented as a model) Also compared to human performance Results ...
Computational Aspects of Incrementally Objective Algorithms for
... solution in large increments at the expense of suboptimal rate of convergence and consequently increased number of iterations. The third strategy is based on reducing the number of iterations within an increment by employing smaller load steps with approximate and inexpensive tangent, but at the cos ...
... solution in large increments at the expense of suboptimal rate of convergence and consequently increased number of iterations. The third strategy is based on reducing the number of iterations within an increment by employing smaller load steps with approximate and inexpensive tangent, but at the cos ...
stochastic local search. - International Center for Computational Logic
... . the search space S (π ), which is a finite set of candidate solutions s ∈ S (π ); . a set of solutions S 0(π ) ⊆ S (π ); . a neighbourhood relation on S (π ): N (π ) ⊆ S (π ) × S (π ); . a finite set of memory states M (π ); . an initialization function init(π ) : → D (S (π ) × M (π )); . a step f ...
... . the search space S (π ), which is a finite set of candidate solutions s ∈ S (π ); . a set of solutions S 0(π ) ⊆ S (π ); . a neighbourhood relation on S (π ): N (π ) ⊆ S (π ) × S (π ); . a finite set of memory states M (π ); . an initialization function init(π ) : → D (S (π ) × M (π )); . a step f ...
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., ...
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.