Reducing goal state divergence with environment design
dc.contributor.author | Sikes, Kelsey, author | |
dc.contributor.author | Sreedharan, Sarath, advisor | |
dc.contributor.author | Blanchard, Nathaniel, committee member | |
dc.contributor.author | Chong, Edwin K.P., committee member | |
dc.date.accessioned | 2025-09-01T10:42:02Z | |
dc.date.available | 2025-09-01T10:42:02Z | |
dc.date.issued | 2025 | |
dc.description.abstract | At the core of most successful human-robot collaborations is alignment between a robot's behavior and a human's expectations. Achieving this alignment is often difficult, however, because without careful specification, a robot may misinterpret a human's goals, causing it to perform actions with unexpected, if not dangerous side effects. To avoid this, I propose a new metric called Goal State Divergence (GSD), which represents the difference between the final goal state achieved by a robot and the one a human user expected. In cases where GSD cannot be directly calculated, I show how it can be approximated using maximal and minimal bounds. I then leverage GSD in my novel human-robot goal alignment design (HRGAD) problem, which identifies a minimal set of environment modifications that can reduce such mismatches. To illustrate the effectiveness of my method for reducing goal state divergence, I then empirically evaluate it on several standard planning benchmarks. | |
dc.format.medium | born digital | |
dc.format.medium | masters theses | |
dc.identifier | Sikes_colostate_0053N_19072.pdf | |
dc.identifier.uri | https://hdl.handle.net/10217/241759 | |
dc.identifier.uri | https://doi.org/10.25675/3.02079 | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Colorado State University. Libraries | |
dc.relation.ispartof | 2020- | |
dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
dc.subject | classical planning | |
dc.subject | goal recognition | |
dc.subject | planning and scheduling | |
dc.subject | environment design | |
dc.subject | automated planning | |
dc.subject | human-robot interaction | |
dc.title | Reducing goal state divergence with environment design | |
dc.type | Text | |
dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Colorado State University | |
thesis.degree.level | Masters | |
thesis.degree.name | Master of Science (M.S.) |
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