{"id":{"repo_id":"windsor","oai_identifier":"oai:uwindsor.scholaris.ca:20.500.14776/6312"},"canonical_url":"https://search.dev.ndltd.org/etd/windsor/oai:uwindsor.scholaris.ca:20.500.14776/6312","repository":{"repo_id":"windsor","name":"University of Windsor","base_url":"https://uwindsor.scholaris.ca/server/oai/request"},"display":{"title":"Trend Analysis of Belief-State History with Discrete Wavelet Transform for Improved Intention Discovery","abstract":"Software Product Lines (SPL) have emerged as a new paradigm of software development. By means of mass production of customized software products, SPL has the potential to significantly reduce development time and cost while improving the quality of software systems. Currently, there is still a severe shortage of tools that support the decision-making process for software clients to interactively \"order\" software products due to the difficulty of software customization, especially via dialogue in natural language. While most of the existing approaches use POMDP-based dialogue management, this thesis research proposes to introduce historical information of belief states into the POMDP model and to analyze its trend with discrete wavelet transformation (DWT). Accordingly, a new algorithm is developed to improve the accuracy of intention discovery with trend analysis, and to reduce the dialog length by switching POMDP policies between contextual control modes according to the anticipated knowledge of different users. The efficiency and accuracy of the proposed method are examined by experiments with simulation.","abstract_html":"Software Product Lines (SPL) have emerged as a new paradigm of software development. By means of mass production of customized software products, SPL has the potential to significantly reduce development time and cost while improving the quality of software systems. Currently, there is still a severe shortage of tools that support the decision-making process for software clients to interactively "order" software products due to the difficulty of software customization, especially via dialogue in natural language. While most of the existing approaches use POMDP-based dialogue management, this thesis research proposes to introduce historical information of belief states into the POMDP model and to analyze its trend with discrete wavelet transformation (DWT). Accordingly, a new algorithm is developed to improve the accuracy of intention discovery with trend analysis, and to reduce the dialog length by switching POMDP policies between contextual control modes according to the anticipated knowledge of different users. The efficiency and accuracy of the proposed method are examined by experiments with simulation.","abstract_has_math":false,"creators":["Mulpuri, VIjaya Krishna"],"institution":"University of Windsor","degree_name":"M.Sc.","degree_level":"Masters","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["YUAN, XIAOBU"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01","date_published":"2016-01-01","updated_at":"2026-07-27T22:04:56Z","subjects":[],"languages":["en_CA"],"rights":[],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14776/6312","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["YUAN, XIAOBU"]},{"key":"dc:creator","label":"Author","values":["Mulpuri, VIjaya Krishna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-24 12:41"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-10-26 11:47","2025-06-24T16:41:10Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-01-01"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/masterThesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.Sc."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Windsor"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_CA"]},{"key":"dc:rights","label":"Dc Rights","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14776/6312"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Software Product Lines (SPL) have emerged as a new paradigm of software development. By means of mass production of customized software products, SPL has the potential to significantly reduce development time and cost while improving the quality of software systems. Currently, there is still a severe shortage of tools that support the decision-making process for software clients to interactively \"order\" software products due to the difficulty of software customization, especially via dialogue in natural language. While most of the existing approaches use POMDP-based dialogue management, this thesis research proposes to introduce historical information of belief states into the POMDP model and to analyze its trend with discrete wavelet transformation (DWT). Accordingly, a new algorithm is developed to improve the accuracy of intention discovery with trend analysis, and to reduce the dialog length by switching POMDP policies between contextual control modes according to the anticipated knowledge of different users. 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Currently, there is still a severe shortage of tools that support the decision-making process for software clients to interactively \"order\" software products due to the difficulty of software customization, especially via dialogue in natural language. While most of the existing approaches use POMDP-based dialogue management, this thesis research proposes to introduce historical information of belief states into the POMDP model and to analyze its trend with discrete wavelet transformation (DWT). Accordingly, a new algorithm is developed to improve the accuracy of intention discovery with trend analysis, and to reduce the dialog length by switching POMDP policies between contextual control modes according to the anticipated knowledge of different users. 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