DeepAI AI Chat
Log In Sign Up

Online Elicitation of Necessarily Optimal Matchings

12/08/2021
by   Jannik Peters, et al.
Berlin Institute of Technology (Technische Universität Berlin)
0

In this paper, we study the problem of eliciting preferences of agents in the house allocation model. For this we build on a recent model of Hosseini et al.[AAAI'21] and focus on the task of eliciting preferences to find matchings which are necessarily optimal, i.e., optimal under all possible completions of the elicited preferences. In particular, we follow the approach of Hosseini et al. and investigate the elicitation of necessarily Pareto optimal (NPO) and necessarily rank-maximal (NRM) matchings. Most importantly, we answer their open question and give an online algorithm for eliciting an NRM matching in the next-best query model which is 3/2-competitive, i.e., it takes at most 3/2 as many queries as an optimal algorithm. Besides this, we extend this field of research by introducing two new natural models of elicitation and by studying both the complexity of determining whether a necessarily optimal matching exists in them, and by giving online algorithms for these models.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/17/2020

Learning Desirable Matchings From Partial Preferences

We study the classic problem of matching n agents to n objects, where th...
11/14/2022

Massively Parallel Algorithms for b-Matching

This paper presents an O(loglogd̅) round massively parallel algorithm fo...
12/04/2020

On the best-choice prophet secretary problem

We study a variant of the secretary problem where candidates come from i...
05/14/2019

Online Computation with Untrusted Advice

The advice model of online computation captures the setting in which the...
06/07/2019

Holistic evaluation of XML queries with structural preferences on an annotated strong dataguide

With the emergence of XML as de facto format for storing and exchanging ...
02/12/2020

A Matching Mechanism with Anticipatory Tolls for Congestion Pricing

This paper presents a matching mechanism for assigning drivers to routes...
09/23/2022

The complexity of unsupervised learning of lexicographic preferences

This paper considers the task of learning users' preferences on a combin...