Dynamic programming for predict+optimise

Emir Demirović, Peter J. Stuckey, Tias Guns, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Jeffrey Chan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. We provide a novel learning technique for predict+optimise to directly reason about the underlying combinatorial optimisation problem, offering a meaningful integration of machine learning and optimisation. This is done by representing the combinatorial problem as a piecewise linear function parameterised by the coefficients of the learning model and then iteratively performing coordinate descent on the learning coefficients. Our approach is applicable to linear learning functions and any optimisation problem solvable by dynamic programming. We illustrate the effectiveness of our approach on benchmarks from the literature.

Original languageEnglish
Title of host publicationAAAI 2020 - Thirty-Fourth AAAI Conference on Artificial Intelligence
EditorsVincent Conitzer, Fei Sha
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages1444-1451
Number of pages8
ISBN (Electronic)9781577358350
DOIs
Publication statusPublished - 2020
EventAAAI Conference on Artificial Intelligence 2020 - New York, United States of America
Duration: 7 Feb 202012 Feb 2020
Conference number: 34th
https://aaai.org/Conferences/AAAI-20/ (Website)

Publication series

NameAAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence
Number2
Volume34
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence 2020
Abbreviated titleAAAI-20
CountryUnited States of America
CityNew York
Period7/02/2012/02/20
OtherThe Thirty-Fourth AAAI Conference on Artificial Intelligence was held on February 7–12, 2020 in New York, New York, USA. The surge in public interest in AI technologies, which we have witnessed over the past few years, continued to accelerate in 2019–2020, with the societal and economic impact of AI becoming a central point of public and government discussion worldwide. AAAI-20 saw submissions and attendance numbers that were records in the history of the AAAI series of conferences and continued its tradition of attracting top-quality papers from all areas of AI. We were excited to see increases in submissions across almost all areas.

The AAAI-20 program consisted of a core technical program of original research presentations, including a special track on AI for social impact and a sister conference track. It additionally featured a broad range of tutorials, workshops, invited talks, panels, student abstracts, a debate, and presentations by senior members. The program was rounded out by technical demonstrations, exhibits, an AI job fair, the AI in Practice program, a student outreach program, and a game night. The conference also continued its tradition of colocating with the long-running IAAI conference and the EAAI symposium, as well as the newer conference on AI, Ethics, and Society.
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