Algorithm for optimal winner determination in combinatorial auctions
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TLDR
The algorithm allows combinatorial auctions to scale up to significantly larger numbers of items and bids than prior approaches to optimal winner determination by capitalizing on the fact that the space of bids is sparsely populated in practice.About:
This article is published in Artificial Intelligence.The article was published on 2002-02-01 and is currently open access. It has received 1045 citations till now. The article focuses on the topics: Combinatorial auction & Common value auction.read more
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Proceedings Article
An Iterative Generalized Vickrey Auction: Strategy-Proofness without Complete Revelation
TL;DR: It is shown that in each round agents must only bid for the set of bundles that maximize their utility given current ask prices, which does not require agents to compute their exact values for every bundle.
TBBL: A Tree-Based Bidding Language for Iterative Combinatorial Exchanges
Ruggiero Cavallo,David C. Parkes,Adam I. Juda,Adam Kirsch,Alex Kulesza,Sébastien Lahaie,Benjamin Lubin,Loizos Michael,Jeffery Shneidman +8 more
TL;DR: A novel tree-based logical bidding language, TBBL, for preference elicitation in combinatorial exchanges (CEs) with rich semantics, making it exponentially more concise than OR* and LGB for preferences that are realistic in important domains for CEs.
Journal ArticleDOI
A memetic algorithm for the optimal winner determination problem
TL;DR: This paper investigates a new selection strategy based on both fitness and diversity to choose individuals to participate in the reproduction phase of the memetic algorithm and enhances the algorithm by using a stochastic local search component combined with a specific crossover operator.
Proceedings ArticleDOI
Effectiveness of Query Types and Policies for Preference Elicitation in Combinatorial Auctions
Benoît Hudson,Tuomas Sandholm +1 more
TL;DR: It is proved that randomization helps, in that no deterministic elicitor is a universal revelation reducer and a new query type is presented that allows agents to use anytime algorithms to give approximate answers that are refined only as needed.
Journal ArticleDOI
Genetic Scheduling and Reinforcement Learning in Multirobot Systems for Intelligent Warehouses
Jiajia Dou,Chunlin Chen,Pei Yang +2 more
TL;DR: A new hybrid solution is presented to improve the efficiency of intelligent warehouses with multirobot systems, where the genetic algorithm (GA) based task scheduling is combined with reinforcement learning (RL) based path planning for mobile robots.
References
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Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book ChapterDOI
Reducibility Among Combinatorial Problems
TL;DR: The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible.
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Integer programming
TL;DR: The principles of integer programming are directed toward finding solutions to problems from the fields of economic planning, engineering design, and combinatorial optimization as mentioned in this paper, which is a standard of graduate-level courses since 1972.
Journal ArticleDOI
Incentives in Teams
TL;DR: This paper analyzes the problem of inducing the members of an organization to behave as if they formed a team and exhibits a particular set of compensation rules, an optimal incentive structure, that leads to team behavior.