Smart predict-and-optimize for hard combinatorial optimization problems

Jayanta Mandi, Emir Demirović, Peter J. Stuckey, Tias Guns

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

2 Citations (Scopus)

Abstract

Combinatorial optimization assumes that all parameters of the optimization problem, e.g. the weights in the objective function, are fixed. Often, these weights are mere estimates and increasingly machine learning techniques are used to for their estimation. Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems. It takes the regret of the predictions on the linear problem into account, by repeatedly solving it during learning. We investigate the use of SPO to solve more realistic discrete optimization problems. The main challenge is the repeated solving of the optimization problem. To this end, we investigate ways to relax the problem as well as warm-starting the learning and the solving. Our results show that even for discrete problems it often suffices to train by solving the relaxation in the SPO loss. Furthermore, this approach outperforms the state-of-the-art approach of Wilder, Dilkina, and Tambe. We experiment with weighted knapsack problems as well as complex scheduling problems, and show for the first time that a predict-and-optimize approach can successfully be used on large-scale combinatorial optimization problems.

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)
Pages1603-1610
Number of pages8
ISBN (Electronic)9781577358350
DOIs
Publication statusPublished - 2020
Externally publishedYes
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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