{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/7676"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/7676","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Conditional Preference Networks: Learning and Optimization","abstract":"The last two decades have shown a great body of work in the eld of Arti cial Intelligence (AI) addressing issues related to representing, reasoning and learning preferences. One of the main models for graphical representation of preferences is that of Conditional Preference Networks (CP-nets). A CP-net de nes a partial order over the set of outcomes or alternatives by providing a concise set of small preference statements. Since their introduction, CP-nets have been intensively studied and applied in various problems involving preferences. This thesis is concerned with two main issues related to CP-nets: learning and optimization. Concerning the learning aspect, we determine the information complexity of learning acyclic CP-nets in di erent models. We also consider the problem of learning CP-nets from queries and provide query strategies that are shown to be near-optimal. With regard to the optimization part, we study the problem of solving a constrained CP-net, i.e., a CP-net where some outcomes are infeasible. Our main goal is to nd at least one outcome that is feasible but not dominated with respect to the induced order of the CP-net, i.e., a Pareto set. We study the e ect of variable ordering heuristics and constraint propagation to the problem and show that a variable ordering heuristic augmented with constraint propagation yields a saving to the solving process.","abstract_html":"The last two decades have shown a great body of work in the eld of Arti cial Intelligence (AI) addressing issues related to representing, reasoning and learning preferences. One of the main models for graphical representation of preferences is that of Conditional Preference Networks (CP-nets). A CP-net de nes a partial order over the set of outcomes or alternatives by providing a concise set of small preference statements. Since their introduction, CP-nets have been intensively studied and applied in various problems involving preferences. This thesis is concerned with two main issues related to CP-nets: learning and optimization. Concerning the learning aspect, we determine the information complexity of learning acyclic CP-nets in di erent models. We also consider the problem of learning CP-nets from queries and provide query strategies that are shown to be near-optimal. With regard to the optimization part, we study the problem of solving a constrained CP-net, i.e., a CP-net where some outcomes are infeasible. Our main goal is to nd at least one outcome that is feasible but not dominated with respect to the induced order of the CP-net, i.e., a Pareto set. We study the e ect of variable ordering heuristics and constraint propagation to the problem and show that a variable ordering heuristic augmented with constraint propagation yields a saving to the solving process.","abstract_has_math":false,"creators":["Alanazi, Eisa Ayed"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral -- first","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Mouhoub, Malek"],"committee_chairs":[],"committee_members":["El-Darieby, Mohamed","Zilles, Sandra","Butz, Cory","Sadaoui-Mouhoub, Samira"],"year":2016,"date_issued":"2016-12","date_published":"2016-12","updated_at":"2026-07-24T04:03:32Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4114"],"render_values":[{"text":"https://doi.org/10.82465/4114","href":"https://doi.org/10.82465/4114","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/7676","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Mouhoub, Malek"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["El-Darieby, Mohamed","Zilles, Sandra","Butz, Cory","Sadaoui-Mouhoub, Samira"]},{"key":"dc:creator","label":"Author","values":["Alanazi, Eisa Ayed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-06-19T22:19:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-06-19T22:19:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-12"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral -- first"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4114"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/7676"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfilment of the Requirements For the Degree of Doctor of Philosophy In Computer Science, University of Regina. x, 107 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["The last two decades have shown a great body of work in the eld of Arti cial Intelligence (AI) addressing issues related to representing, reasoning and learning preferences. One of the main models for graphical representation of preferences is that of Conditional Preference Networks (CP-nets). A CP-net de nes a partial order over the set of outcomes or alternatives by providing a concise set of small preference statements. Since their introduction, CP-nets have been intensively studied and applied in various problems involving preferences. This thesis is concerned with two main issues related to CP-nets: learning and optimization. Concerning the learning aspect, we determine the information complexity of learning acyclic CP-nets in di erent models. We also consider the problem of learning CP-nets from queries and provide query strategies that are shown to be near-optimal. With regard to the optimization part, we study the problem of solving a constrained CP-net, i.e., a CP-net where some outcomes are infeasible. Our main goal is to nd at least one outcome that is feasible but not dominated with respect to the induced order of the CP-net, i.e., a Pareto set. We study the e ect of variable ordering heuristics and constraint propagation to the problem and show that a variable ordering heuristic augmented with constraint propagation yields a saving to the solving process."]},{"key":"dc:title","label":"Title","values":["Conditional Preference Networks: Learning and Optimization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Mouhoub, Malek"],"dc:contributor.committeemember":["El-Darieby, Mohamed","Zilles, Sandra","Butz, Cory","Sadaoui-Mouhoub, Samira"],"dc:creator":["Alanazi, Eisa Ayed"],"dc:date.accessioned":["2017-06-19T22:19:26Z"],"dc:date.available":["2017-06-19T22:19:26Z"],"dc:date.issued":["2016-12"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfilment of the Requirements For the Degree of Doctor of Philosophy In Computer Science, University of Regina. x, 107 p."],"dc:description.abstract":["The last two decades have shown a great body of work in the eld of Arti cial Intelligence (AI) addressing issues related to representing, reasoning and learning preferences. 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Our main goal is to nd at least one outcome that is feasible but not dominated with respect to the induced order of the CP-net, i.e., a Pareto set. We study the e ect of variable ordering heuristics and constraint propagation to the problem and show that a variable ordering heuristic augmented with constraint propagation yields a saving to the solving process."],"dc:identifier.doi":["https://doi.org/10.82465/4114"],"dc:identifier.uri":["https://hdl.handle.net/10294/7676"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Conditional Preference Networks: Learning and Optimization"],"dc:type":["master thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Doctoral -- first"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:32Z"}