Using Strongly Connected Components as a Basis for Autonomous Skill Acquisition in Reinforcement Learning

2009 ◽  
pp. 794-803 ◽  
Author(s):  
Seyed Jalal Kazemitabar ◽  
Hamid Beigy
Keyword(s):  
Reinforcement Learning ◽  
Skill Acquisition ◽  
Connected Components ◽  
Strongly Connected Components ◽  
Strongly Connected

ENHANCING ADEQUACY OF GRADING STUDY PROJECTS ON THE BASE OF PARAMETRIC RELAXATION OF PAIRWISE COMPARISONS

2021 ◽  
Vol 1 ◽  
pp. 122-133
Author(s):  
Alexey V. Oletsky ◽  
◽  
Mikhail F. Makhno ◽  
◽  
Keyword(s):  
Center Of Mass ◽  
Estimation Algorithm ◽  
Relaxation Parameter ◽  
Connected Components ◽  
Pairwise Comparisons ◽  
Strongly Connected Components ◽  
Standard Scale ◽  
Relaxation Parameters ◽  
Strongly Connected ◽  
The One

A problem of automated assessing of students’ study projects is regarded. A heuristic algorithm based on fuzzy estimating of projects and on pairwise comparisons among them is proposed. For improving adequacy and naturalness of grades, an approach based on introducing a parameter named relaxation parameter was suggested in the paper. This enables to reduce the spread between maximum and minimum values of projects in comparison with the one in the standard scale suggested by T. Saati. Reasonable values of this parameter were selected experimentally. For estimating the best alternative, a center of mass of a fuzzy max-min composition should be calculated. An estimation algorithm for a case of non-transitive preferences based on getting strongly connected components and on pairwise comparisons between them is also suggested. In this case, relaxation parameters should be chosen separately for each subtask. So the combined technique of evaluating alternatives proposed in the paper depends of the following parameters: relaxation parameters for pairwise comparisons matrices within each strongly connected components; relaxation parameter for pairwise comparisons matrices among strongly connected components; membership function for describing the best alternative.

A Reconfigurable Architecture for the Detection of Strongly Connected Components

2016 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Osama G. Attia ◽  
Kevin R. Townsend ◽  
Phillip H. Jones ◽  
Joseph Zambreno
Keyword(s):  
Reconfigurable Architecture ◽  
Connected Components ◽  
Strongly Connected Components ◽  
Strongly Connected

Parallel Strongly Connected Components Detection with Multi-partition on GPUs

2019 ◽  
pp. 16-30 ◽  
Author(s):  
Junteng Hou ◽  
Shupeng Wang ◽  
Guangjun Wu ◽  
Ge Fu ◽  
Siyu Jia ◽  
...  
Keyword(s):  
Connected Components ◽  
Strongly Connected Components ◽  
Strongly Connected
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