Hybrid Best-First Greedy Search for Orienteering with Category Constraints

2017 ◽  
pp. 24-42 ◽  
Author(s):  
Paolo Bolzoni ◽  
Sven Helmer
Keyword(s):  
Greedy Search

Greedy Search Methods

2019 ◽  
pp. 241-255
Author(s):  
Max Kuhn ◽  
Kjell Johnson
Keyword(s):  
Greedy Search ◽  
Search Methods

scholarly journals Generalized Potential Heuristics for Classical Planning

2019 ◽  
Author(s):  
Guillem Francès ◽  
Augusto B. Corrêa ◽  
Cedric Geissmann ◽  
Florian Pommerening
Keyword(s):  
Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Order Logic ◽  
First Order Logic ◽  
Mixed Integer ◽  
Weighted Sums ◽  
Greedy Search ◽  
First Order ◽  
Generalized Potential ◽  
New States

Generalized planning aims at computing solutions that work for all instances of the same domain. In this paper, we show that several interesting planning domains possess compact generalized heuristics that can guide a greedy search in guaranteed polynomial time to the goal, and which work for any instance of the domain. These heuristics are weighted sums of state features that capture the number of objects satisfying a certain first-order logic property in any given state. These features have a meaningful interpretation and generalize naturally to the whole domain. Additionally, we present an approach based on mixed integer linear programming to compute such heuristics automatically from the observation of small training instances. We develop two variations of the approach that progressively refine the heuristic as new states are encountered. We illustrate the approach empirically on a number of standard domains, where we show that the generated heuristics will correctly generalize to all possible instances.

scholarly journals Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network

2021 ◽  
Vol 3 (4) ◽  
pp. 922-945
Author(s):  
Shaw-Hwa Lo ◽  
Yiqiao Yin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Language Processing ◽  
Text Classification ◽  
Search Algorithm ◽  
Greedy Search ◽  
Text Documents ◽  
Engineering Technique ◽  
Language Semantics ◽  
Sequential Models

Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for making good predictions in text classification tasks. Next, a greedy search algorithm, called the Backward Dropping Algorithm, is proposed to handle long-term dependencies in the dataset. Moreover, the paper proposes a novel engineering technique called the “dagger technique” that fully preserves the relationship between the explanatory variable and the response variable. The proposed techniques can be further generalized into any feed-forward Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and any neural network. A real-world application on the Internet Movie Database (IMDB) is used and the proposed methods are applied to improve prediction performance with an 81% error reduction compared to other popular peers if I-score and “dagger technique” are not implemented.

Investigation of Environmental Monitoring Designs for Corporate Management Information Systems

2010 ◽  
pp. 138-154 ◽  
Author(s):  
Marina G. Erechtchoukova ◽  
Stephen Y. Chen ◽  
Peter A. Khaiter
Keyword(s):  
Environmental Management ◽  
Information System ◽  
Management Information System ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Corporate Management ◽  
Greedy Search ◽  
Management Information ◽  
Corporate Environmental Management ◽  
Search Approach

The evaluation of an organization’s environmental performance is an integral part of a corporate environmental management information system. This chapter considers an organization’s environmental impact assessment with respect to a water resource. It investigates formal approaches to the development of temporal monitoring designs for producing data sufficient to perform the assessment. In this study, simple random sampling, stratified random sampling, and designs obtained using greedy search have been investigated with respect to their compatibility with a corporate environmental management information system. All three approaches determine temporal monitoring designs with minimal costs and supply data sufficient for estimation of water quality indicators for a given level of uncertainty. It is shown that monitoring designs obtained using the greedy search approach will outperform other designs when the level of uncertainty in the estimate must be low. If high levels of uncertainty are tolerable, simple random designs become preferable due to their simplicity and effectiveness. The proposed approaches lead to automated procedures which can be easily integrated into a corporate environmental management information system.

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