scholarly journals ASAP: An Automatic Algorithm Selection Approach for Planning

2014 ◽  
Vol 23 (06) ◽  
pp. 1460032 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Diane Kitchin
Keyword(s):  
Dimensional Space ◽  
Automated Planning ◽  
Search Space ◽  
Algorithm Selection ◽  
Selection Approach ◽  
Automatic Algorithm Selection ◽  
The Given ◽  
Automatic Algorithm ◽  
Made In

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.

scholarly journals Effect of utilization term on the quality of semi-natural grass stand in the autumn and in the winter time

2004 ◽  
Vol 52 (1) ◽  
pp. 127-136
Author(s):  
Jiří Skládanka
Keyword(s):  
Climatic Conditions ◽  
Phleum Pratense ◽  
Green Fodder ◽  
Grass Stand ◽  
Forage Utilization ◽  
Winter Time ◽  
June July ◽  
The Given ◽  
Made In

Quality of green fodder from a semi-natural sward consisting of Festuca rubra, Taraxacum officinale, Dactylis glomerata, Trisetum flavescens, Poa ssp., Agrostis stolonifera and Phleum pratense as dominant species, situated in the Bohemian-Moravian Highlands (Czech Republic) was studied in the months of the main forage utilization (November, December and January). Main usage in the winter months was preceded by usage in June, July and August (preparatory cut). The sward was fertilized in the first half of August with 50 kg N.ha-1. Qualitative characteristics studied in 2000/2001, 2001/2002 and 2002/2003 were N-substances and NEL. Sward quality was decreasing from November to January with the date of utilization exhibiting a highly significant effect (α < 0.01) on the NEL content in all three years of monitoring and on the content of N-substances in the first two years of monitoring. Effect of the preparatory cut on the contents of N-substances and NEL was significant (α < 0.05) in all three years of monitoring and in the first two years of monitoring, respectively. In November, the contents of N-substances and NEL were higher in variants with the preparatory cut made in August than in variants with the preparatory cut made in June or July. The effect of the date of preparatory cut on the contents of N-substances and NEL in December and January was depending on climatic conditions in the given year.

Anytime automatic algorithm selection for knapsack

2020 ◽  
Vol 158 ◽  
pp. 113613 ◽  
Author(s):  
Isaías I. Huerta ◽  
Daniel A. Neira ◽  
Daniel A. Ortega ◽  
Vicente Varas ◽  
Julio Godoy ◽  
...  
Keyword(s):  
Algorithm Selection ◽  
Selection For ◽  
Automatic Algorithm Selection ◽  
Automatic Algorithm

scholarly journals Improving Univariate Time Series Anomaly Detection Through Automatic Algorithm Selection and Human-in-the-Loop False-Positive Removement

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Cynthia Freeman ◽  
Ian Beaver ◽  
Abdullah Mueen
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
False Positive ◽  
Detection Method ◽  
Algorithm Selection ◽  
Human In The Loop ◽  
Extensive Evaluation ◽  
Univariate Time Series ◽  
Automatic Algorithm Selection ◽  
Automatic Algorithm

The existence of a time series anomaly detection method that performs well for all domains is a myth. Given a massive library of available methods, how can one select the best method for their application? An extensive evaluation of every anomaly detection method is not feasible. Many existing anomaly detection systems do not include an avenue for human feedback, essential given the subjective nature of what even is anomalous. We present a technique for improving univariate time series anomaly detection through automatic algorithm selection and human-in-the-loop false-positive removement. These determinations were made by extensively experimenting with over 30 pre-annotated time series from the open-source Numenta Anomaly Benchmark repository. Once the highest performing anomaly detection methods are selected via these characteristics, humans can annotate the predicted outliers which are used to tune anomaly scores via subsequence similarity search and improve the selected methods for their data, increasing evaluation scores and reducing the need for annotation by 70% on predicted anomalies where annotation is used to improve F-scores.

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