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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Journal ArticleDOI
Equilibria of Greedy Combinatorial Auctions
Brendan Lucier,Allan Borodin +1 more
TL;DR: It is shown, for a variety of equilibrium concepts, including Bayes--Nash equilibria, low-regret bidding sequences, and asynchronous best-response dynamics, that the resulting price of anarchy bound is close to the approximation factor of the underlying greedy algorithm.
Proceedings ArticleDOI
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch
TL;DR: This work proposes a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm and uses imitation learning to bootstrap an RL agent and then uses Proximal Policy Optimization (PPO) to further explore global optimal actions.
Auction-Based Models for Composite Service Selection: A Design Framework.
TL;DR: An emerging trend of composite service selection approaches based on auction models benefit from the dynamic pricing of auction models compared to a fixed pricing approach and have the potential to incorporate the dependencies that exist between services constituting a composition.
Proceedings ArticleDOI
Fail-Stop Distributed Combinatorial Auctioning Systems with fair resource allocation
TL;DR: This work systematically analyze the failures occurring in the agents and proposes a fail-stop design of the DCAS, and presents a set of Event Based Failure Handlers (EBFHs) that are triggered upon receiving specific messages from other agents in the network.
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.
Book
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.