An Approximate Algorithm for Resource Allocation Using Combinatorial Auctions

Avasarala, Viswanath; Polavarapu, Himanshu; Mullen, Tracy

doi:10.1109/iat.2006.33

Cited by 22 publications

(24 citation statements)

References 7 publications

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“…An NP-hard problem (Rothkopf et al 1998), the winner determination problem has been tackled in the literature with various approaches (Sandholm 2002;Sandholm et al 2005;Fujishima et al 1999;Zurel and Nisan 2001;Avasarala et al 2006;Hoos and Boutilier 2000). Another related research direction, auction design is about designing different protocol variants for combinatorial auctions (Parkes and Ungar 2000;Cramton et al 2006;Yokoo et al 2006;Rastegari et al 2009;Ausubel et al 2006;Lehmann et al 1999;Nisan and Ronen 2000;Lavi et al 2003;Archer et al 2004;Holland and O'Sullivan 2007).…”

Section: Strategies In Combinatorial Auctionsmentioning

confidence: 98%

Adaptive bidding in combinatorial auctions in dynamic markets

Sui

Leung

2011

Evolving Systems

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Combinatorial auction, where bidders can bid on bundles of items, has been the subject of increasing interest in recent years. Although much research work has been conducted on combinatorial auctions, most has been focusing on the winner determination problem. A largely unexplored area of research in combinatorial auctions is the design of bidding strategies, in particular, those that can be used in open and dynamic markets in which market situation is generally constantly changing. Obviously, a good bidding strategy to be used in such realistic markets cannot be obtained by analytical methods, which require all market information be known before a solution can possibly be found. Machine learning based approaches are not completely appropriate, either, as an optimal strategy learned from one market generally no longer perform well when the market situation changes. In this paper, we propose a new adaptive bidding strategy for combinatorial auctionbased resource allocation problem in such dynamic markets. A bidder adopting this strategy constantly perceives the market situation, and adaptively reviews and adjusts his bid determination, thus responding to the dynamic market in a timely manner. Experiment results show that agents adopting this adaptive bidding strategy perform very well, even without any prior knowledge about the market.

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Section: Strategies In Combinatorial Auctionsmentioning

confidence: 98%

Adaptive bidding in combinatorial auctions in dynamic markets

Sui

Leung

2011

Evolving Systems

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“…L2 Number of items in a bid: uniform, price offer: linearly random with low_linearly = 0, hi_linearly = 1 (when bid price is defined as a float value), or low_linearly = 500, hi_linearly = 1500 (when bid price is defined 2 Since some of the original data seems corrupted or failed to obtain optimal values, we excluded such auction problems from our dataset. Also, we excluded a whole dataset of a specific bid distribution when the number of valid optimal values is smaller than the other half of the data.…”

Section: Evaluation On Basic Auction Datasetmentioning

confidence: 99%

“…In [2], Avasarala et al proposed an approach for the winner determination problem on combinatorial auctions. However, in [2], they noticed that their algorithm is not effective for approximation in short time but is effective for obtaining higher optimal solutions with enough computation time. Random-walk searching is really effective approximation approach for combina-torial optimization problems.…”

Section: Approaches For Optimization Problemsmentioning

confidence: 99%

Fine-grained efficient resource allocation using approximated combinatorial auctions

Fukuta

Ito

2009

Web Intelligence and Agent Systems: An International Journal

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Combinatorial auctions, one of the most popular market mechanisms, have a huge effect on electronic markets and political strategies. Combinatorial auctions provide suitable mechanisms for efficient allocation of resources to self-interested attendees. On the other hand, efficient resource allocation is also becoming crucial in many computer systems that should manage resources efficiently. Considering ubiquitous computing scenarios, the ability to complete an auction within a fine-grained time period without loss of allocation efficiency is in strong demand. Furthermore, to achieve such scenarios, it is very important to handle a large number of bids in an auction. In general, the optimal winner determination problem of a combinatorial auction is NP-hard. Thus, much work focuses on tackling the computational costs for winner determination. In this paper, we show that our approximation algorithms provide sufficient quality of winners for auctions that have a large number of bids on hard time constraints. Furthermore, we compare and discuss desirable properties of such approximation algorithms to be embedded in application systems.

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“…For large sensor networks, we have formulated a special Genetic algorithm that solves the bid translation problem in polynomial time [13]. The representation schema used for the algorithms resembles the SGA algorithm that we developed for determining combinatorial auction winners [15]. First, let the number of bids be k and the number of sensors be n. The representation schema used by the Genetic algorithm is a string of length n, where each string element is a real-valued number between 1 and k. The j th string member represents the bid to which sensor j is allocated.…”

Section: Equation 2 : Calculation Of Bid-price For Resource Set S At mentioning

confidence: 99%

An experimental study on agent learning for market-based sensor management

Avasarala¹,

Mullen²,

Hall³

et al. 2009

2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making

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Distributed Sensor management, the process of managing or coordinating the use of sensing resources in a distributed environment, is a multi-objective optimization problem. In our earlier work, we proposed MASM (Market-Architecture for Sensor Management), a market-based approach to allocate sensor resources in real-time to various resource requestors. MASM models the multi-objective sensor management problem as a combinatorial-auction based market where the network resources sell goods to the resource requestors. To allow the resource requestors to participate in the market, MASM grants "budgets" to these resource requestors based on their priority to the overall mission. However, for a given budget, self-interested resource requestors or buyers can learn from market-data and adapt their bidding behavior. This paper presents results of an initial experimental study, where the learning behavior of resource requestors is modeled and their effect on market performance is examined.

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An Approximate Algorithm for Resource Allocation Using Combinatorial Auctions

Cited by 22 publications

References 7 publications

Adaptive bidding in combinatorial auctions in dynamic markets

Adaptive bidding in combinatorial auctions in dynamic markets

Fine-grained efficient resource allocation using approximated combinatorial auctions

An experimental study on agent learning for market-based sensor management

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