scholarly journals Automation in Handling Uncertainty in Semantic-knowledge based Robotic Task-planning by Using Markov Logic Networks

2014 ◽  
Vol 35 ◽  
pp. 1023-1032 ◽  
Author(s):  
Ahmed Al-Moadhen ◽  
Michael Packianather ◽  
Rossi Setchi ◽  
Renxi Qiu
2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.


2020 ◽  
pp. 1097-1120
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael S. Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.


Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler

Author(s):  
Omar Adjali ◽  
Amar Ramdane-Cherif

This article describes a semantic framework that demonstrates an approach for modeling and reasoning based on environment knowledge representation language (EKRL) to enhance interaction between robots and their environment. Unlike EKRL, standard Binary approaches like OWL language fails to represent knowledge in an expressive way. The authors show in this work how to: model environment and interaction in an expressive way with first-order and second-order EKRL data-structures, and reason for decision-making thanks to inference capabilities based on a complex unification algorithm. This is with the understanding that robot environments are inherently subject to noise and partial observability, the authors extended EKRL framework with probabilistic reasoning based on Markov logic networks to manage uncertainty.


2019 ◽  
Vol 40 (05) ◽  
pp. 344-358
Author(s):  
Elizabeth Spencer Kelley ◽  
Howard Goldstein

AbstractVocabulary knowledge of young children, as a well-established predictor of later reading comprehension, is an important domain for assessment and intervention. Standardized, knowledge-based measures are commonly used by speech-language pathologists (SLPs) to describe existing vocabulary knowledge and to provide comparisons to same-age peers. Process-based assessments of word learning can be helpful to provide information about how children may respond to learning opportunities and to inform treatment decisions. This article presents an exploratory study of the relation among vocabulary knowledge, word learning, and learning in vocabulary intervention in preschool children. The study examines the potential of a process-based assessment of word learning to predict response to vocabulary intervention. Participants completed a static, knowledge-based measure of vocabulary knowledge, a process-based assessment of word learning, and between 3 and 11 weeks of vocabulary intervention. Vocabulary knowledge, performance on the process-based assessment of word learning, and learning in vocabulary intervention were strongly related. SLPs might make use of the information provided by a process-based assessment of word learning to determine the appropriate intensity of intervention and to identify areas of phonological and semantic knowledge to target during intervention.


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