dc.contributor.author | Ling, Chun Kai | |
dc.contributor.author | Low, Kian Hsiang | |
dc.contributor.author | Jaillet, Patrick | |
dc.date.accessioned | 2017-12-22T14:55:33Z | |
dc.date.available | 2017-12-22T14:55:33Z | |
dc.date.issued | 2016-02 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/112929 | |
dc.description.abstract | This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defining new tasks/problems. In particular, it utilizes a principled Bayesian sequential decision problem framework for jointly and naturally optimizing the exploration-exploitation trade-off. In general, the resulting induced GPP policy cannot be derived exactly due to an uncountable set of candidate observations. A key contribution of our work here thus lies in exploiting the Lipschitz continuity of the reward functions to solve for a nonmyopic adaptive ε-optimal GPP (ε-GPP) policy. To plan in real time, we further propose an asymptotically optimal, branch-and-bound anytime variant of ε-GPP with performance guarantee. We empirically demonstrate the effectiveness of our ε-GPP policy and its anytime variant in Bayesian optimization and an energy harvesting task. | en_US |
dc.description.sponsorship | Singapore-MIT Alliance for Research and Technology (SMART) (52 R-252-000-550-592) | en_US |
dc.language.iso | en_US | |
dc.publisher | Association for Computing Machinery | en_US |
dc.relation.isversionof | http://dl.acm.org/citation.cfm?id=3016159 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | MIT Web Domain | en_US |
dc.title | Gaussian Process Planning with Lipschitz Continuous Reward Functions | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Ling, Chun Kai, Kian Hsiang Low and Patrick Jaillet. "Gaussian process planning with lipschitz continuous reward functions: towards unifying Bayesian optimization, active learning, and beyond." Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence AAAI'16, 12-17 February, 2016, Phoenix, Arizona, Association for Computing Machinery, 2016. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.mitauthor | Jaillet, Patrick | |
dc.relation.journal | Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16 | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dspace.orderedauthors | Ling, Chun Kai; Low, Kian Hsiang; Jaillet, Patrick | en_US |
dspace.embargo.terms | N | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-8585-6566 | |
mit.license | OPEN_ACCESS_POLICY | en_US |