[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118615-en":3,"doc-seo-118615-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118615,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",6,"Technology","Coupling Machine Learning with Ontology for Robotics Applications","A practical approach couples machine learning (ML) algorithms with knowledge-bases using ontology formalism to overcome missing prior knowledge in dynamic robotic scenarios. The method models a two-tiers intelligence interaction in which trained ML models extract knowledge from live sensor data when knowledge is not available in the ontology layer. Two experiments use different datasets and risk-awareness tasks analyzed by backpropagation neural networks, Naive Bayes, and a J48 decision tree. Results show computational validity and linear time complexity with respect to data and knowledge size during robot missions.","Coupling Machine Learning with Ontology for  \nRobotics Applications  \nOsama F. Zaki1,2  \n1 Robotaar, Livingston, EH54 6DD, Scotland, UK, [osama.zaki@robotaar.com](osama.zaki@robotaar.com)  \n2 Sinai University, Arish, Sinai, Egypt, [osama.farouk@su.edu.eg](osama.farouk@su.edu.eg)  \nAbstract  \nIn this paper I present a practical approach for coupling machine learning (ML) algorithms with knowledge bases (KB) ontology formalism. The lack of availability of prior knowledge in dynamic scenarios is without doubt a major barrier for scalable machine intelligence. My view of the interaction between the two tiers intelligence is based on the idea that when knowledge is not readily available at the knowledge base tier, more knowledge can be extracted from the other tier, which has access to trained models from machine learning algorithms. To analyse this hypothesis, I create two experiments based on different datasets, which are related directly to risk-awareness of autonomous systems, analysed by different machine learning algorithms (namely; multi-layer feedforward backpropagation, Naive Bayes, and J48 decision tree). My analysis shows that the two-tiers intelligence approach for coupling ML and KB is computationally valid and the time complexity of the algorithms during the robot mission is linear with the size of the data and knowledge.  \nKey words: trust AI; machine learning; neural; symbolic systems  \n1. Introduction  \nTrust in the reliability and resilience of autonomous systems is paramount to their continued growth, as well as their safe and effective utilization [32][9][7] . Hauser [13] reported the need for intelligent autonomous systems – based on AI and ML – operating in real-world conditions to radically improve their resilience and capability to recover from damage. Rich [23] expressed the view that there is a prospect for AI and ML to solve many of those problems. Cave and Dihal [5] claimed that a balanced view of intelligent systems by understanding the positive and negative merits will have impact in the way they are deployed, applied, and regulated in real-world environments.  \nAI and robotics researchers have applied ontology as a knowledge-based scheme, within a system to support robotics autonomy, such as SMERobotics [20], KnowRob 2.0 [30], CARESSES [4], open-EASE [3], ORO [27][17], SIARAS [12] . They covered a spectrum of cognitive functions, which according to the classification made by [16] and [28] are recognition and categorization, decision making and choice, perception and situation assessment, prediction and monitoring, problem solving and planning, reasoning and belief maintenance, execution and action, interaction, and communication, and remembering, reflection, and learning. The ontology scope of these prior works varies, and it depends on the functionalities of the target robotic system, i.e. concepts that were modelled in the ontology are related to: object names, environment, affordance, action and task, activity and behaviour, plan and method, capability and skill, hardware components, software components, interaction, and communication [18][33] . Primary motivation for the use of ontologies within robotics is that these knowledge-based approaches offer an expandable and adaptable approach for capturing the semantic features of model robot cognitive capabilities. Furthermore, when considering a fleet distribution of robotic platforms, or swarms, the ontology provides a cyber-physical interface to cloud, web-based  \nservice robots, such as RoboEarth [29] and openEASE [2] that enable robots to collect and share knowledge of missions. This knowledge enabled architecture provides a means of sharing knowledge via the ontology, between different robots, and between different subsystems of a single robot’s control system in a machine understandable and consistent presentation. Therefore, attempts have been made to create CORA (Core Ontology for Robotics and Automation), which was developed in the context of","cbCais8IFEiyPX4f","https://ap.wps.com/l/cbCais8IFEiyPX4f","pdf",1240277,1,14,"English","en",105,"# Introduction\n## Ontologies for robotics autonomy\n## Two-tiers intelligence motivation\n## ML-driven knowledge extraction for risk awareness\n## Battery health and ontology population","[{\"question\":\"What problem does the paper address in robotics knowledge and AI?\",\"answer\":\"It targets the barrier caused by the lack of readily available prior knowledge in dynamic scenarios, which limits scalable machine intelligence for autonomous systems.\"},{\"question\":\"How does the proposed two-tiers approach combine ML and ontology?\",\"answer\":\"When knowledge is not available in the knowledge-base/ontology tier, the approach extracts additional knowledge using trained ML models that operate with access to data from sensors.\"},{\"question\":\"Which algorithms and experiments are used to validate the hypothesis?\",\"answer\":\"Two experiments are built from different datasets and risk-awareness tasks, evaluated with multi-layer feedforward backpropagation, Naive Bayes, and a J48 decision tree.\"}]","Coupling Machine Learning with Ontology for Robotics Applications | 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problem does the paper address in robotics knowledge and AI?","Question",{"text":76,"@type":77},"It targets the barrier caused by the lack of readily available prior knowledge in dynamic scenarios, which limits scalable machine intelligence for autonomous systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed two-tiers approach combine ML and ontology?",{"text":81,"@type":77},"When knowledge is not available in the knowledge-base/ontology tier, the approach extracts additional knowledge using trained ML models that operate with access to data from sensors.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithms and experiments are used to validate the hypothesis?",{"text":85,"@type":77},"Two experiments are built from different datasets and risk-awareness tasks, evaluated with multi-layer feedforward backpropagation, Naive Bayes, and a J48 decision 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