[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118136-en":3,"doc-seo-118136-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118136,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Supporting Competitive Robot Game Mission Planning Using Machine Learning","This dissertation studies how machine learning can support strategic planning and mission execution in competitive robot games, with a focus on the FIRST LEGO® League (FLL). It develops guidelines for evaluating mission strategies using machine learning within the FLL context, based on a literature review of current FLL mission strategy design practices and of machine learning methods, especially evolutionary algorithms. The work analyzes genetic algorithms for mission strategy evaluation and builds a prototype system to simulate and assess strategies for practical use by FLL teams. Results indicate genetic algorithms can identify effective mission strategies and enable real-time feedback for improved decision-making.","SUPPORTING COMPETITIVE ROBOT GAME MISSION PLANNING USING MACHINE LEARNING  \nby  \nElton Strydom  \n2024  \nSUPPORTING COMPETITIVE ROBOT GAME MISSION PLANNING USING MACHINE LEARNING  \nby  \nElton Strydom  \nDissertation  \nsubmitted in fulfillment  \nof the requirements  \nfor the degree  \nMaster of Information Technology  \nin the  \nFaculty of Engineering  \nof the  \nNelson Mandela University  \nSupervisor: Prof. Bertram Haskins  \nCo-supervisor: Dr. Ronald Leppan  \nApril 2024  \n2024-03-10  \nPERMISSION TO SUBMIT FINAL COPIES  \nOF TREATISE/DISSERTATION/THESIS TO THE ASSESSMENT AND GRADUATION OFFICE  \nPlease type or complete in black ink  \nFACULTY:  Faculty of Engineering, the Built Environment and Technology  SCHOOL/DEPARTMENT:  School of IT I, (surname and initials of supervisor)  Haskins, BP  and (surname and initials of co-supervisor)  Leppan, RG  the supervisor and co-supervisor respectively for (surname and initials of  \ncandidate) Strydom, E (student number) s215030281  a candidate for the (full description of qualification) Master of Information Technology  \n\n| with a treatise/dissertation/thesis entitled (full title of treatise/dissertation/thesis):\u003Cbr>SUPPORTING COMPETITIVE ROBOT GAME MISSION PLANNING USING |\n| --- |\n| MACHINE LEARNING |\n\nIt is hereby certified that the proposed amendments to the treatise/dissertation/thesis have been effected and that permission is granted to the candidate to submit the final bound copies of his/her treatise/dissertation/thesis to the examination office.  \n11/03/2024  \nSUPERVISOR DATE  \nAnd  \n11/03/2024  \nCO-SUPERVISOR DATE  \nDeclaration  \nI, Elton Strydom, hereby declare that:  \n• The work in this dissertation is my work.  \n• All sources used or referred to have been documented and recognised.  \n• This dissertation has not previously been submitted in full or partial fulfillment of the requirements for an equivalent or higher qualification at anyother recognised educational institute.  \nElton Strydom  \nAbstract  \nThis dissertation presents a study aimed at supporting the strategic planning and execution of missions in competitive robot games, particularly in the FIRST LEGO® League (FLL), through the use of machine learning techniques. The primary objective is to formulate guidelines for evaluating mission strategies using machine learning techniques within the FLL landscape, thereby supporting participants in the mission strategy design journey within the FLL robot game. The research methodology encompasses a literature review, focusing on the current practices in the FLL mission strategy design process. This is followed by a literature review of machine learning techniques on a broad level pivoting towards evolutionary algorithms. The study then delves into the specifics of genetic algorithms, exploring their suitability and potential advantages for mission strategy evaluation in competitive robotic environments within the FLL robot game. A significant portion of the research involves the development and testing of a prototype system that applies a genetic algorithm to simulate and evaluate different mission strategies, providing a practical tool for FLL teams. During the development of the evaluation prototype, guidelines were formulated aligning with the primary research objective which is to formulate guidelines for evaluating mission strategies in robot games using machine learning techniques. Key findings of this study highlight the effectiveness of genetic algorithms in identifying optimal mission strategies. The prototype demonstrates the feasibility of using machine learning to provide real-time, feedback to participating teams, enabling more informed decision-making in the formulation of mission strategies.  \nAcknowledgements  \nI would like to express my sincere gratitude to the following individuals:  \nProf. Bertram Haskins, for his invaluable guidance in research and the field of machine learning. Additionally, my thanks to Dr. Ronald Leppan for his expertise and counsel in FLL. The ","cbCailQitzOe25lE","https://ap.wps.com/l/cbCailQitzOe25lE","pdf",9887555,1,125,"English","en",105,"# 1 Introduction\n## 1.1 Introduction\n## 1.2 FIRST® LEGO® League\n## 1.3 Machine Learning\n## 1.4 Evolutionary Algorithms\n## 1.5 Problem Area\n## 1.6 Research Objectives\n## 1.7 Research Design\n## 1.8 Research Methodology\n## 1.9 Scope and Delineation\n## 1.10 Preliminary Layout Of The Dissertation\n## 1.11 Conclusion\n# 2 FIRST® LEGO® League Challenge\n## 2.1 Introduction","[{\"question\":\"What is the main purpose of the dissertation?\",\"answer\":\"To support strategic planning and mission execution in competitive robot games, especially FIRST LEGO® League (FLL), by formulating guidelines that evaluate mission strategies using machine learning.\"},{\"question\":\"Which machine learning methods are investigated for mission strategy evaluation?\",\"answer\":\"The research focuses on evolutionary algorithms, with detailed attention to genetic algorithms and their suitability for evaluating mission strategies in competitive robotic environments.\"},{\"question\":\"What does the developed prototype system do?\",\"answer\":\"It applies a genetic algorithm to simulate and evaluate different mission strategies, providing a practical tool for FLL teams.\"}]","Supporting Competitive Robot Game Mission Planning Using Machine Learning | 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