{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/106007"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/106007","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Designing interaction for human-machine collaboration in multi-agent scheduling","abstract":"In the field of multi-agent task scheduling, there are many algorithms that are capable of minimizing objective functions when the user is able to specify them. However, there is a need for systems and algorithms that are able to include user preferences or domain knowledge into the final solution. This will increase the usability of algorithms that would otherwise not include some characteristics desired by the end user but are highly optimal mathematically. We hypothesize that allowing subjects to iterate over solutions while adding allocation and temporal constraints would allow them to take advantage of the computational power to solve the temporal problem while including their preferences. No statistically significant results were found that supported that such algorithm is preferred over manually solving the problem among the participants. However, there are trends that support the hypothesis. We found statistically significant evidence (p=0.0027), that subjects reported higher workload when working with Manual Mode and Modification Mode rather than Iteration Mode and Feedback Iteration Mode. We propose changes to the system that can provide guidance for future design of interaction for scheduling problems.","abstract_html":"In the field of multi-agent task scheduling, there are many algorithms that are capable of minimizing objective functions when the user is able to specify them. However, there is a need for systems and algorithms that are able to include user preferences or domain knowledge into the final solution. This will increase the usability of algorithms that would otherwise not include some characteristics desired by the end user but are highly optimal mathematically. We hypothesize that allowing subjects to iterate over solutions while adding allocation and temporal constraints would allow them to take advantage of the computational power to solve the temporal problem while including their preferences. No statistically significant results were found that supported that such algorithm is preferred over manually solving the problem among the participants. However, there are trends that support the hypothesis. We found statistically significant evidence (p=0.0027), that subjects reported higher workload when working with Manual Mode and Modification Mode rather than Iteration Mode and Feedback Iteration Mode. We propose changes to the system that can provide guidance for future design of interaction for scheduling problems.","abstract_has_math":false,"creators":["Perez, Jorge (Jorge I.)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Julie Shah."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-22T22:21:29Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/106007"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 57-58)."]},{"key":"dc:description.abstract","label":"Abstract","values":["In the field of multi-agent task scheduling, there are many algorithms that are capable of minimizing objective functions when the user is able to specify them. However, there is a need for systems and algorithms that are able to include user preferences or domain knowledge into the final solution. This will increase the usability of algorithms that would otherwise not include some characteristics desired by the end user but are highly optimal mathematically. We hypothesize that allowing subjects to iterate over solutions while adding allocation and temporal constraints would allow them to take advantage of the computational power to solve the temporal problem while including their preferences. No statistically significant results were found that supported that such algorithm is preferred over manually solving the problem among the participants. However, there are trends that support the hypothesis. We found statistically significant evidence (p=0.0027), that subjects reported higher workload when working with Manual Mode and Modification Mode rather than Iteration Mode and Feedback Iteration Mode. We propose changes to the system that can provide guidance for future design of interaction for scheduling problems."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Designing interaction for human-machine collaboration in multi-agent scheduling"]}]}],"canonical_facts":{"dc:contributor.advisor":["Julie Shah."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Perez, Jorge (Jorge I.)"],"dc:date.accessioned":["2016-12-22T15:18:20Z"],"dc:date.available":["2016-12-22T15:18:20Z"],"dc:date.issued":["2015"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 57-58)."],"dc:description.abstract":["In the field of multi-agent task scheduling, there are many algorithms that are capable of minimizing objective functions when the user is able to specify them. However, there is a need for systems and algorithms that are able to include user preferences or domain knowledge into the final solution. This will increase the usability of algorithms that would otherwise not include some characteristics desired by the end user but are highly optimal mathematically. We hypothesize that allowing subjects to iterate over solutions while adding allocation and temporal constraints would allow them to take advantage of the computational power to solve the temporal problem while including their preferences. No statistically significant results were found that supported that such algorithm is preferred over manually solving the problem among the participants. However, there are trends that support the hypothesis. We found statistically significant evidence (p=0.0027), that subjects reported higher workload when working with Manual Mode and Modification Mode rather than Iteration Mode and Feedback Iteration Mode. We propose changes to the system that can provide guidance for future design of interaction for scheduling problems."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/106007"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Designing interaction for human-machine collaboration in multi-agent scheduling"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:29Z"}